Split (1)

train · 107k rows

id stringlengths 8 78	source stringclasses 743 values	chunk_id int64 1 5.05k	text stringlengths 593 49.7k
app-as-api-008	app-as-api.pdf	8	ID. Example: workspacespool/wspool-123456. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes ScalableDimension The scalable dimension associated with the scalable target. This string consists of the service namespace, resource type, and scaling property. • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. Request Parameters API Version 2016-02-06 21 Application Auto Scaling API Reference • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits Request Parameters API Version 2016-02-06 22 Application Auto Scaling API Reference \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: Yes ServiceNamespace The namespace of the AWS service that provides the resource. For a resource provided by your own application or service, use custom-resource instead. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes Response Elements If the action is successful, the service sends back an HTTP 200 response with an empty HTTP body. Errors For information about the errors that are common to all actions, see Common Errors. ConcurrentUpdateException Concurrent updates caused an exception, for example, if you request an update to an Application Auto Scaling resource that already has a pending update. Response Elements API Version 2016-02-06 23 Application Auto Scaling HTTP Status Code: 400 InternalServiceException The service encountered an internal error. HTTP Status Code: 400 ObjectNotFoundException API Reference The speciﬁed object could not be found. For any operation that depends on the existence of a scalable target, this exception is thrown if the scalable target with the speciﬁed service namespace, resource ID, and scalable dimension does not exist. For any operation that deletes or deregisters a resource, this exception is thrown if the resource cannot be found. HTTP Status Code: 400 ValidationException An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 Examples If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a signature. For more information, see Signing AWS API requests in the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Example The following example deregisters a scalable target for an Amazon ECS service called web-app that is running in the default cluster. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.DeregisterScalableTarget Examples API Version 2016-02-06 24 Application Auto Scaling X-Amz-Date: 20190506T210150Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 API Reference Authorization: AUTHPARAMS { "ResourceId": "service/my-cluster/my-service", "ServiceNamespace": "ecs", "ScalableDimension": "ecs:service:DesiredCount" } See Also For more information about using this API in one
app-as-api-009	app-as-api.pdf	9	the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Example The following example deregisters a scalable target for an Amazon ECS service called web-app that is running in the default cluster. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.DeregisterScalableTarget Examples API Version 2016-02-06 24 Application Auto Scaling X-Amz-Date: 20190506T210150Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 API Reference Authorization: AUTHPARAMS { "ResourceId": "service/my-cluster/my-service", "ServiceNamespace": "ecs", "ScalableDimension": "ecs:service:DesiredCount" } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 25 Application Auto Scaling API Reference DescribeScalableTargets Gets information about the scalable targets in the speciﬁed namespace. You can ﬁlter the results using ResourceIds and ScalableDimension. Request Syntax { "MaxResults": number, "NextToken": "string", "ResourceIds": [ "string" ], "ScalableDimension": "string", "ServiceNamespace": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MaxResults The maximum number of scalable targets. This value can be between 1 and 50. The default value is 50. If this parameter is used, the operation returns up to MaxResults results at a time, along with a NextToken value. To get the next set of results, include the NextToken value in a subsequent call. If this parameter is not used, the operation returns up to 50 results and a NextToken value, if applicable. Type: Integer Required: No NextToken The token for the next set of results. Type: String DescribeScalableTargets API Version 2016-02-06 26 Application Auto Scaling API Reference Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No ResourceIds The identiﬁer of the resource associated with the scalable target. This string consists of the resource type and unique identiﬁer. • ECS service - The resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:document-classifier-endpoint/EXAMPLE. • Amazon Comprehend entity recognizer endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:entity-recognizer-endpoint/EXAMPLE. Request Parameters API Version 2016-02-06 27 Application Auto Scaling API Reference • Lambda provisioned concurrency - The resource type is function and the unique identiﬁer is the function name with a function version or alias name suﬃx that is not $LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID.
app-as-api-010	app-as-api.pdf	10	Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Type: Array of strings Array Members: Maximum number of 50 items. Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No ScalableDimension The scalable dimension associated with the scalable target. This string consists of the service namespace, resource type, and scaling property. If you specify a scalable dimension, you must also specify a resource ID. Request Parameters API Version 2016-02-06 28 Application Auto Scaling API Reference • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. Request Parameters API Version 2016-02-06 29 Application Auto Scaling API Reference • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: No ServiceNamespace The namespace of the AWS service that provides the resource. For a resource provided by your own application or service, use custom-resource instead. Request Parameters API Version 2016-02-06 30 Application Auto Scaling Type: String API Reference Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes Response Syntax { "NextToken": "string", "ScalableTargets": [ { "CreationTime": number, "MaxCapacity": number, "MinCapacity": number, "PredictedCapacity": number, "ResourceId": "string", "RoleARN": "string", "ScalableDimension": "string", "ScalableTargetARN": "string", "ServiceNamespace": "string", "SuspendedState": { "DynamicScalingInSuspended": boolean, "DynamicScalingOutSuspended": boolean, "ScheduledScalingSuspended": boolean } } ] } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. NextToken The token required to get the next set of results. This value is null if there are no more results to return. Response Syntax API Version 2016-02-06 31 Application Auto Scaling Type: String API Reference Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* ScalableTargets The scalable targets that match the request parameters. Type: Array of ScalableTarget objects Errors For information about the errors that are common to all actions, see Common Errors. ConcurrentUpdateException Concurrent updates caused an exception, for example, if you request an update to an Application Auto Scaling
app-as-api-011	app-as-api.pdf	11	an HTTP 200 response. The following data is returned in JSON format by the service. NextToken The token required to get the next set of results. This value is null if there are no more results to return. Response Syntax API Version 2016-02-06 31 Application Auto Scaling Type: String API Reference Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* ScalableTargets The scalable targets that match the request parameters. Type: Array of ScalableTarget objects Errors For information about the errors that are common to all actions, see Common Errors. ConcurrentUpdateException Concurrent updates caused an exception, for example, if you request an update to an Application Auto Scaling resource that already has a pending update. HTTP Status Code: 400 InternalServiceException The service encountered an internal error. HTTP Status Code: 400 InvalidNextTokenException The next token supplied was invalid. HTTP Status Code: 400 ValidationException An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 Examples If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a signature. For more information, see Signing AWS API requests in Errors API Version 2016-02-06 32 Application Auto Scaling API Reference the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Example The following example describes the scalable targets for the ecs service namespace. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.DescribeScalableTargets X-Amz-Date: 20190506T184921Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "ServiceNamespace": "ecs" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: [request-id] Content-Type: application/x-amz-json-1.1 Content-Length: 272 Date: Fri, 06 May 2019 18:49:21 GMT { "ScalableTargets": [ { "CreationTime": 1462558906.199, "MaxCapacity": 10, "MinCapacity": 1, "ResourceId": "service/my-cluster/my-service", "RoleARN": "arn:aws:iam::012345678910:role/ aws-service-role/ecs.application-autoscaling.amazonaws.com/ AWSServiceRoleForApplicationAutoScaling_ECSService", "ScalableDimension": "ecs:service:DesiredCount", "ServiceNamespace": "ecs", Examples API Version 2016-02-06 33 Application Auto Scaling API Reference "SuspendedState": { "DynamicScalingInSuspended": false, "DynamicScalingOutSuspended": false, "ScheduledScalingSuspended": false } } ] } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 34 Application Auto Scaling API Reference DescribeScalingActivities Provides descriptive information about the scaling activities in the speciﬁed namespace from the previous six weeks. You can ﬁlter the results using ResourceId and ScalableDimension. For information about viewing scaling activities using the AWS CLI, see Scaling activities for Application Auto Scaling. Request Syntax { "IncludeNotScaledActivities": boolean, "MaxResults": number, "NextToken": "string", "ResourceId": "string", "ScalableDimension": "string", "ServiceNamespace": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. IncludeNotScaledActivities Speciﬁes whether to include activities that aren't scaled (not scaled activities) in the response. Not scaled activities are activities that aren't completed or started for various reasons, such as preventing inﬁnite scaling loops. For help interpreting the not scaled reason details in the response, see Scaling activities for Application Auto Scaling. Type: Boolean Required: No MaxResults The maximum number of scalable targets. This value can be between 1 and 50. The default value is 50. DescribeScalingActivities API Version 2016-02-06 35 Application Auto Scaling API Reference If this parameter is used, the operation returns up to MaxResults results at a time, along with a NextToken value. To get the next set of results, include the NextToken value in a subsequent call. If this parameter is not used, the operation returns up to 50 results and a NextToken value, if applicable. Type: Integer Required: No NextToken The token for the next set of results. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No ResourceId The identiﬁer of the resource associated with the scaling activity. This string consists of the resource type and unique identiﬁer. • ECS service - The resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora
app-as-api-012	app-as-api.pdf	12	spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. Request Parameters API Version 2016-02-06 36 Application Auto Scaling API Reference • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:document-classifier-endpoint/EXAMPLE. • Amazon Comprehend entity recognizer endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:entity-recognizer-endpoint/EXAMPLE. • Lambda provisioned concurrency - The resource type is function and the unique identiﬁer is the function name with a function version or alias name suﬃx that is not $LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Request Parameters API Version 2016-02-06 37 Application Auto Scaling Type: String API Reference Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No ScalableDimension The scalable dimension. This string consists of the service namespace, resource type, and scaling property. If you specify a scalable dimension, you must also specify a resource ID. • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. Request Parameters API Version 2016-02-06 38 Application Auto Scaling API Reference • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an
app-as-api-013	app-as-api.pdf	13	write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits Request Parameters API Version 2016-02-06 39 Application Auto Scaling API Reference \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: No ServiceNamespace The namespace of the AWS service that provides the resource. For a resource provided by your own application or service, use custom-resource instead. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes Response Syntax { "NextToken": "string", "ScalingActivities": [ { "ActivityId": "string", "Cause": "string", "Description": "string", "Details": "string", "EndTime": number, "NotScaledReasons": [ { "Code": "string", "CurrentCapacity": number, "MaxCapacity": number, "MinCapacity": number } ], "ResourceId": "string", Response Syntax API Version 2016-02-06 40 Application Auto Scaling API Reference "ScalableDimension": "string", "ServiceNamespace": "string", "StartTime": number, "StatusCode": "string", "StatusMessage": "string" } ] } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. NextToken The token required to get the next set of results. This value is null if there are no more results to return. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* ScalingActivities A list of scaling activity objects. Type: Array of ScalingActivity objects Errors For information about the errors that are common to all actions, see Common Errors. ConcurrentUpdateException Concurrent updates caused an exception, for example, if you request an update to an Application Auto Scaling resource that already has a pending update. HTTP Status Code: 400 InternalServiceException The service encountered an internal error. Response Elements API Version 2016-02-06 41 Application Auto Scaling HTTP Status Code: 400 InvalidNextTokenException The next token supplied was invalid. HTTP Status Code: 400 ValidationException API Reference An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 Examples If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a signature. For more information, see Signing AWS API requests in the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Example of scaling activities for a scaling policy The following example describes the scaling activities for an Amazon ECS service named web- app that's running in the default cluster. It shows the scaling activities for the scaling policy named cpu75-target-tracking-scaling-policy, initiated by the CloudWatch alarm named TargetTracking-service/my-cluster/my-service-AlarmHigh-d4f0770c-b46e-434a- a60f-3b36d653feca. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.DescribeScalingActivities X-Amz-Date: 20190506T224112Z User-Agent: aws-cli/1.10.26 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS Examples API Version 2016-02-06 42 Application Auto Scaling API Reference { "ResourceId": "service/my-cluster/my-service", "ServiceNamespace": "ecs", "ScalableDimension": "ecs:service:DesiredCount" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: [request-id] Content-Type: application/x-amz-json-1.1 Content-Length: 1784 Date: Fri, 06 May 2019 22:41:12 GMT { "ScalingActivities": [ { "ScalableDimension": "ecs:service:DesiredCount", "Description": "Setting desired count to 3.", "ResourceId": "service/my-cluster/my-service", "ActivityId": "4d759079-a31f-4d0c-8468-504c56e2eecf", "StartTime": 1462574194.658, "ServiceNamespace": "ecs", "EndTime": 1462574276.686, "Cause": "monitor alarm TargetTracking-service/my-cluster/my-service-AlarmHigh- d4f0770c-b46e-434a-a60f-3b36d653feca in state ALARM triggered policy cpu75-target- tracking-scaling-policy", "StatusMessage": "Successfully set desired count to 3. Change successfully fulfilled by ecs.", "StatusCode": "Successful" }, { "ScalableDimension": "ecs:service:DesiredCount", "Description": "Setting desired count to 2.", "ResourceId": "service/my-cluster/my-service", "ActivityId": "90aff0eb-dd6a-443c-889b-b809e78061c1", "StartTime": 1462574254.223, "ServiceNamespace": "ecs", "EndTime": 1462574333.492, "Cause": "monitor alarm TargetTracking-service/my-cluster/my-service-AlarmHigh- d4f0770c-b46e-434a-a60f-3b36d653feca in state ALARM triggered policy cpu75-target- tracking-scaling-policy", Examples API Version 2016-02-06 43 Application Auto Scaling API Reference "StatusMessage": "Successfully set desired count to 2. Change successfully fulfilled by ecs.", "StatusCode": "Successful" } ] } Example of scaling activities for scheduled actions The following example describes the scaling activities for a DynamoDB table named my-table. It shows the scaling activities for scheduled actions named my-first-scheduled-action and my- second-scheduled-action. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.DescribeScalingActivities X-Amz-Date: 20190526T110828Z User-Agent: aws-cli/1.10.26 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "ResourceId": "table/my-table", "ServiceNamespace":
app-as-api-014	app-as-api.pdf	14	"Cause": "monitor alarm TargetTracking-service/my-cluster/my-service-AlarmHigh- d4f0770c-b46e-434a-a60f-3b36d653feca in state ALARM triggered policy cpu75-target- tracking-scaling-policy", Examples API Version 2016-02-06 43 Application Auto Scaling API Reference "StatusMessage": "Successfully set desired count to 2. Change successfully fulfilled by ecs.", "StatusCode": "Successful" } ] } Example of scaling activities for scheduled actions The following example describes the scaling activities for a DynamoDB table named my-table. It shows the scaling activities for scheduled actions named my-first-scheduled-action and my- second-scheduled-action. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.DescribeScalingActivities X-Amz-Date: 20190526T110828Z User-Agent: aws-cli/1.10.26 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "ResourceId": "table/my-table", "ServiceNamespace": "dynamodb", "ScalableDimension": "dynamodb:table:WriteCapacityUnits" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: [request-id] Content-Type: application/x-amz-json-1.1 Content-Length: 1784 Date: Fri, 26 May 2019 11:08:28 GMT { "ScalingActivities": [ { Examples API Version 2016-02-06 44 Application Auto Scaling API Reference "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting write capacity units to 10.", "ResourceId": "table/my-table", "ActivityId": "4d1308c0-bbcf-4514-a673-b0220ae38547", "StartTime": 1561574415.086, "ServiceNamespace": "dynamodb", "Cause": "maximum capacity was set to 10", "StatusMessage": "Successfully set write capacity units to 10. Waiting for change to be fulfilled by dynamodb.", "StatusCode": "InProgress" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting min capacity to 5 and max capacity to 10", "ResourceId": "table/my-table", "ActivityId": "f2b7847b-721d-4e01-8ef0-0c8d3bacc1c7", "StartTime": 1561574414.644, "ServiceNamespace": "dynamodb", "Cause": "scheduled action name my-second-scheduled-action was triggered", "StatusMessage": "Successfully set min capacity to 5 and max capacity to 10", "StatusCode": "Successful" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting write capacity units to 15.", "ResourceId": "table/my-table", "ActivityId": "d8ea4de6-9eaa-499f-b466-2cc5e681ba8b", "StartTime": 1561574108.904, "ServiceNamespace": "dynamodb", "EndTime": 1561574140.255, "Cause": "minimum capacity was set to 15", "StatusMessage": "Successfully set write capacity units to 15. Change successfully fulfilled by dynamodb.", "StatusCode": "Successful" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting min capacity to 15 and max capacity to 20", "ResourceId": "table/my-table", "ActivityId": "3250fd06-6940-4e8e-bb1f-d494db7554d2", "StartTime": 1561574108.512, "ServiceNamespace": "dynamodb", "Cause": "scheduled action name my-first-scheduled-action was triggered", "StatusMessage": "Successfully set min capacity to 15 and max capacity to 20", Examples API Version 2016-02-06 45 Application Auto Scaling API Reference "StatusCode": "Successful" } ] } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 46 Application Auto Scaling API Reference DescribeScalingPolicies Describes the Application Auto Scaling scaling policies for the speciﬁed service namespace. You can ﬁlter the results using ResourceId, ScalableDimension, and PolicyNames. For more information, see Target tracking scaling policies and Step scaling policies in the Application Auto Scaling User Guide. Request Syntax { "MaxResults": number, "NextToken": "string", "PolicyNames": [ "string" ], "ResourceId": "string", "ScalableDimension": "string", "ServiceNamespace": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MaxResults The maximum number of scalable targets. This value can be between 1 and 10. The default value is 10. If this parameter is used, the operation returns up to MaxResults results at a time, along with a NextToken value. To get the next set of results, include the NextToken value in a subsequent call. If this parameter is not used, the operation returns up to 10 results and a NextToken value, if applicable. Type: Integer Required: No NextToken The token for the next set of results. DescribeScalingPolicies API Version 2016-02-06 47 Application Auto Scaling Type: String API Reference Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No PolicyNames The names of the scaling policies to describe. Type: Array of strings Array Members: Maximum number of 50 items. Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No ResourceId The identiﬁer of the resource associated with the scaling policy. This string consists of the resource type and unique identiﬁer. • ECS service - The resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. Request Parameters
app-as-api-015	app-as-api.pdf	15	type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. Request Parameters API Version 2016-02-06 48 Application Auto Scaling API Reference • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:document-classifier-endpoint/EXAMPLE. • Amazon Comprehend entity recognizer endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:entity-recognizer-endpoint/EXAMPLE. • Lambda provisioned concurrency - The resource type is function and the unique identiﬁer is the function name with a function version or alias name suﬃx that is not $LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Request Parameters API Version 2016-02-06 49 Application Auto Scaling Type: String API Reference Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No ScalableDimension The scalable dimension. This string consists of the service namespace, resource type, and scaling property. If you specify a scalable dimension, you must also specify a resource ID. • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. Request Parameters API Version 2016-02-06 50 Application Auto Scaling API Reference • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool.
app-as-api-016	app-as-api.pdf	16	Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits Request Parameters API Version 2016-02-06 51 Application Auto Scaling API Reference \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: No ServiceNamespace The namespace of the AWS service that provides the resource. For a resource provided by your own application or service, use custom-resource instead. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes Response Syntax { "NextToken": "string", "ScalingPolicies": [ { "Alarms": [ { "AlarmARN": "string", "AlarmName": "string" } ], "CreationTime": number, "PolicyARN": "string", "PolicyName": "string", "PolicyType": "string", "PredictiveScalingPolicyConfiguration": { "MaxCapacityBreachBehavior": "string", "MaxCapacityBuffer": number, "MetricSpecifications": [ Response Syntax API Version 2016-02-06 52 Application Auto Scaling { "CustomizedCapacityMetricSpecification": { "MetricDataQueries": [ API Reference { "Expression": "string", "Id": "string", "Label": "string", "MetricStat": { "Metric": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Namespace": "string" }, "Stat": "string", "Unit": "string" }, "ReturnData": boolean } ] }, "CustomizedLoadMetricSpecification": { "MetricDataQueries": [ { "Expression": "string", "Id": "string", "Label": "string", "MetricStat": { "Metric": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Namespace": "string" }, "Stat": "string", "Unit": "string" Response Syntax API Version 2016-02-06 53 Application Auto Scaling API Reference }, "ReturnData": boolean } ] }, "CustomizedScalingMetricSpecification": { "MetricDataQueries": [ { "Expression": "string", "Id": "string", "Label": "string", "MetricStat": { "Metric": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Namespace": "string" }, "Stat": "string", "Unit": "string" }, "ReturnData": boolean } ] }, "PredefinedLoadMetricSpecification": { "PredefinedMetricType": "string", "ResourceLabel": "string" }, "PredefinedMetricPairSpecification": { "PredefinedMetricType": "string", "ResourceLabel": "string" }, "PredefinedScalingMetricSpecification": { "PredefinedMetricType": "string", "ResourceLabel": "string" }, "TargetValue": number } ], Response Syntax API Version 2016-02-06 54 Application Auto Scaling API Reference "Mode": "string", "SchedulingBufferTime": number }, "ResourceId": "string", "ScalableDimension": "string", "ServiceNamespace": "string", "StepScalingPolicyConfiguration": { "AdjustmentType": "string", "Cooldown": number, "MetricAggregationType": "string", "MinAdjustmentMagnitude": number, "StepAdjustments": [ { "MetricIntervalLowerBound": number, "MetricIntervalUpperBound": number, "ScalingAdjustment": number } ] }, "TargetTrackingScalingPolicyConfiguration": { "CustomizedMetricSpecification": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Metrics": [ { "Expression": "string", "Id": "string", "Label": "string", "MetricStat": { "Metric": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Namespace": "string" }, Response Syntax API Version 2016-02-06 55 Application Auto Scaling API Reference "Stat": "string", "Unit": "string" }, "ReturnData": boolean } ], "Namespace": "string", "Statistic": "string", "Unit": "string" }, "DisableScaleIn": boolean, "PredefinedMetricSpecification": { "PredefinedMetricType": "string", "ResourceLabel": "string" }, "ScaleInCooldown": number, "ScaleOutCooldown": number, "TargetValue": number } } ] } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. NextToken The token required to get the next set of results. This value is null if there are no more results to return. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* ScalingPolicies Information about the scaling policies. Type: Array of ScalingPolicy objects Response Elements API Version 2016-02-06 56 Application Auto Scaling Errors API Reference For information about the errors that are common to all actions, see Common Errors. ConcurrentUpdateException Concurrent updates caused an exception, for example, if you request an update to an Application Auto Scaling resource that already has a pending update. HTTP Status Code: 400 FailedResourceAccessException Failed access to resources caused an exception. This exception is thrown when Application Auto Scaling is unable to retrieve the alarms associated with a scaling policy due to a client error, for example, if the role ARN speciﬁed for a scalable target does not have permission to call the CloudWatch DescribeAlarms on your behalf. HTTP Status Code: 400 InternalServiceException The service encountered an internal error. HTTP Status Code: 400 InvalidNextTokenException The next token supplied was invalid. HTTP Status Code: 400 ValidationException An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 Examples If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a
app-as-api-017	app-as-api.pdf	17	with a scaling policy due to a client error, for example, if the role ARN speciﬁed for a scalable target does not have permission to call the CloudWatch DescribeAlarms on your behalf. HTTP Status Code: 400 InternalServiceException The service encountered an internal error. HTTP Status Code: 400 InvalidNextTokenException The next token supplied was invalid. HTTP Status Code: 400 ValidationException An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 Examples If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a signature. For more information, see Signing AWS API requests in the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Errors API Version 2016-02-06 57 Application Auto Scaling Example API Reference The following example describes the scaling policies for the ecs service namespace. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.DescribeScalingPolicies X-Amz-Date: 20190506T194435Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "ServiceNamespace": "ecs" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: [request-id] Content-Type: application/x-amz-json-1.1 Content-Length: 1393 Date: Fri, 06 May 2019 19:44:35 GMT { "ScalingPolicies": [ { "Alarms": [ { "AlarmARN": "arn:aws:cloudwatch:us-west-2:012345678910:alarm:step- scaling-alarmhigh-ecs:service/my-cluster/my-service", "AlarmName": "Step-Scaling-AlarmHigh-ECS:service/my-cluster/my- service" } ], "CreationTime": 1462561899.23, "PolicyARN": "arn:aws:autoscaling:us- west-2:012345678910:scalingPolicy:ac542982-cbeb-4294-891c-a5a941dfa787:resource/ecs/ service/my-cluster/my-service:policyName/my-scale-out-policy", Examples API Version 2016-02-06 58 Application Auto Scaling API Reference "PolicyName": "my-scale-out-policy", "PolicyType": "StepScaling", "ResourceId": "service/my-cluster/my-service", "ScalableDimension": "ecs:service:DesiredCount", "ServiceNamespace": "ecs", "StepScalingPolicyConfiguration": { "AdjustmentType": "PercentChangeInCapacity", "Cooldown": 60, "MetricAggregationType": "Average", "StepAdjustments": [ { "MetricIntervalLowerBound": 0, "ScalingAdjustment": 200 } ] } }, { "Alarms": [ { "AlarmARN": "arn:aws:cloudwatch:us-west-2:012345678910:alarm:step- scaling-alarmlow-ecs:service/my-cluster/my-service", "AlarmName": "Step-Scaling-AlarmLow-ECS:service/my-cluster/my- service" } ], "CreationTime": 1462562575.099, "PolicyARN": "arn:aws:autoscaling:us- west-2:012345678910:scalingPolicy:6d8972f3-efc8-437c-92d1-6270f29a66e7:resource/ecs/ service/my-cluster/my-service:policyName/my-scale-in-policy", "PolicyName": "my-scale-in-policy", "PolicyType": "StepScaling", "ResourceId": "service/my-cluster/my-service", "ScalableDimension": "ecs:service:DesiredCount", "ServiceNamespace": "ecs", "StepScalingPolicyConfiguration": { "AdjustmentType": "PercentChangeInCapacity", "Cooldown": 120, "MetricAggregationType": "Average", "MinAdjustmentMagnitude": 1, "StepAdjustments": [ { "MetricIntervalLowerBound": -15, "MetricIntervalUpperBound": 0 Examples API Version 2016-02-06 59 Application Auto Scaling API Reference "ScalingAdjustment": -25, }, { "MetricIntervalUpperBound": -15, "ScalingAdjustment": -50 } ] } } ] } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 60 Application Auto Scaling API Reference DescribeScheduledActions Describes the Application Auto Scaling scheduled actions for the speciﬁed service namespace. You can ﬁlter the results using the ResourceId, ScalableDimension, and ScheduledActionNames parameters. For more information, see Scheduled scaling in the Application Auto Scaling User Guide. Request Syntax { "MaxResults": number, "NextToken": "string", "ResourceId": "string", "ScalableDimension": "string", "ScheduledActionNames": [ "string" ], "ServiceNamespace": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MaxResults The maximum number of scheduled action results. This value can be between 1 and 50. The default value is 50. If this parameter is used, the operation returns up to MaxResults results at a time, along with a NextToken value. To get the next set of results, include the NextToken value in a subsequent call. If this parameter is not used, the operation returns up to 50 results and a NextToken value, if applicable. Type: Integer Required: No NextToken The token for the next set of results. DescribeScheduledActions API Version 2016-02-06 61 Application Auto Scaling Type: String API Reference Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No ResourceId The identiﬁer of the resource associated with the scheduled action. This string consists of the resource type and unique identiﬁer. • ECS service - The resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • Custom resources are not supported with a
app-as-api-018	app-as-api.pdf	18	is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:document-classifier-endpoint/EXAMPLE. Request Parameters API Version 2016-02-06 62 Application Auto Scaling API Reference • Amazon Comprehend entity recognizer endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:entity-recognizer-endpoint/EXAMPLE. • Lambda provisioned concurrency - The resource type is function and the unique identiﬁer is the function name with a function version or alias name suﬃx that is not $LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No ScalableDimension The scalable dimension. This string consists of the service namespace, resource type, and scaling property. If you specify a scalable dimension, you must also specify a resource ID. Request Parameters API Version 2016-02-06 63 Application Auto Scaling API Reference • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. Request Parameters API Version 2016-02-06 64 Application Auto Scaling API Reference • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom-
app-as-api-019	app-as-api.pdf	19	Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: No ScheduledActionNames The names of the scheduled actions to describe. Request Parameters API Version 2016-02-06 65 Application Auto Scaling Type: Array of strings API Reference Array Members: Maximum number of 50 items. Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No ServiceNamespace The namespace of the AWS service that provides the resource. For a resource provided by your own application or service, use custom-resource instead. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes Response Syntax { "NextToken": "string", "ScheduledActions": [ { "CreationTime": number, "EndTime": number, "ResourceId": "string", "ScalableDimension": "string", "ScalableTargetAction": { "MaxCapacity": number, "MinCapacity": number }, "Schedule": "string", "ScheduledActionARN": "string", "ScheduledActionName": "string", "ServiceNamespace": "string", "StartTime": number, "Timezone": "string" Response Syntax API Version 2016-02-06 66 Application Auto Scaling } ] } Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. NextToken The token required to get the next set of results. This value is null if there are no more results to return. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* ScheduledActions Information about the scheduled actions. Type: Array of ScheduledAction objects Errors For information about the errors that are common to all actions, see Common Errors. ConcurrentUpdateException Concurrent updates caused an exception, for example, if you request an update to an Application Auto Scaling resource that already has a pending update. HTTP Status Code: 400 InternalServiceException The service encountered an internal error. HTTP Status Code: 400 InvalidNextTokenException The next token supplied was invalid. Response Elements API Version 2016-02-06 67 Application Auto Scaling HTTP Status Code: 400 ValidationException API Reference An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 Examples If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a signature. For more information, see Signing AWS API requests in the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Example The following example describes the scheduled actions for the dynamodb service namespace. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.DescribeScheduledActions X-Amz-Date: 20190506T194435Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "ServiceNamespace": "dynamodb" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: [request-id] Content-Type: application/x-amz-json-1.1 Content-Length: 834 Examples API Version 2016-02-06 68 Application Auto Scaling API Reference Date: Fri, 06 May 2019 19:44:35 GMT { "ScheduledActions": [ { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Schedule": "at(2019-05-20T18:35:00)", "ResourceId": "table/my-table", "CreationTime": 1561571888.361, "ScheduledActionARN": "arn:aws:autoscaling:us- west-2:123456789012:scheduledAction:2d36aa3b-cdf9-4565-b290-81db519b227d:resource/ dynamodb/table/my-table:scheduledActionName/my-first-scheduled-action", "ScalableTargetAction": { "MinCapacity": 15, "MaxCapacity": 20 }, "ScheduledActionName": "my-first-scheduled-action", "ServiceNamespace": "dynamodb" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Schedule": "at(2019-05-20T18:40:00)", "ResourceId": "table/my-table", "CreationTime": 1561571946.021, "ScheduledActionARN": "arn:aws:autoscaling:us- west-2:123456789012:scheduledAction:2d36aa3b-cdf9-4565-b290-81db519b227d:resource/ dynamodb/table/my-table:scheduledActionName/my-second-scheduled-action", "ScalableTargetAction": { "MinCapacity": 5, "MaxCapacity": 10 }, "ScheduledActionName": "my-second-scheduled-action", "ServiceNamespace": "dynamodb" } ] } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface See Also API Version 2016-02-06 69 API Reference Application Auto Scaling • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 70 Application Auto Scaling API Reference GetPredictiveScalingForecast Retrieves the forecast data for a predictive scaling policy. Load forecasts are predictions of the hourly load values using historical load data from CloudWatch and an analysis of historical trends. Capacity forecasts are represented as predicted values for the minimum capacity that is needed on an hourly basis, based on the hourly load forecast. A minimum of 24 hours of data is required to create the initial forecasts. However, having a full 14 days of historical
app-as-api-020	app-as-api.pdf	20	V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 70 Application Auto Scaling API Reference GetPredictiveScalingForecast Retrieves the forecast data for a predictive scaling policy. Load forecasts are predictions of the hourly load values using historical load data from CloudWatch and an analysis of historical trends. Capacity forecasts are represented as predicted values for the minimum capacity that is needed on an hourly basis, based on the hourly load forecast. A minimum of 24 hours of data is required to create the initial forecasts. However, having a full 14 days of historical data results in more accurate forecasts. Request Syntax { "EndTime": number, "PolicyName": "string", "ResourceId": "string", "ScalableDimension": "string", "ServiceNamespace": "string", "StartTime": number } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EndTime The exclusive end time of the time range for the forecast data to get. The maximum time duration between the start and end time is 30 days. Type: Timestamp Required: Yes PolicyName The name of the policy. Type: String GetPredictiveScalingForecast API Version 2016-02-06 71 Application Auto Scaling API Reference Length Constraints: Minimum length of 1. Maximum length of 256. Pattern: \p{Print}+ Required: Yes ResourceId The identiﬁer of the resource. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes ScalableDimension The scalable dimension. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: Yes Request Parameters API Version 2016-02-06 72 Application Auto Scaling ServiceNamespace API Reference The namespace of the AWS service that provides the resource. For a resource provided by your own application or service, use custom-resource instead. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes StartTime The inclusive start time of the time range for the forecast data to get. At most, the date and time can be one year before the current date and time Type: Timestamp Required: Yes Response Syntax { "CapacityForecast": { "Timestamps": [ number ], "Values": [ number ] }, "LoadForecast": [ { "MetricSpecification": { "CustomizedCapacityMetricSpecification": { "MetricDataQueries": [ { "Expression": "string", "Id": "string", "Label": "string", "MetricStat": { "Metric": { "Dimensions": [ { "Name": "string", Response Syntax API Version 2016-02-06 73 Application Auto Scaling API Reference "Value": "string" } ], "MetricName": "string", "Namespace": "string" }, "Stat": "string", "Unit": "string" }, "ReturnData": boolean } ] }, "CustomizedLoadMetricSpecification": { "MetricDataQueries": [ { "Expression": "string", "Id": "string", "Label": "string", "MetricStat": { "Metric": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Namespace": "string" }, "Stat": "string", "Unit": "string" }, "ReturnData": boolean } ] }, "CustomizedScalingMetricSpecification": { "MetricDataQueries": [ { "Expression": "string", "Id": "string", "Label": "string", "MetricStat": { Response Syntax API Version 2016-02-06 74 Application Auto Scaling API Reference "Metric": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Namespace": "string" }, "Stat": "string", "Unit": "string" }, "ReturnData": boolean } ] }, "PredefinedLoadMetricSpecification": { "PredefinedMetricType": "string", "ResourceLabel": "string" }, "PredefinedMetricPairSpecification": { "PredefinedMetricType": "string", "ResourceLabel": "string" }, "PredefinedScalingMetricSpecification": { "PredefinedMetricType": "string", "ResourceLabel": "string" }, "TargetValue": number }, "Timestamps": [ number ], "Values": [ number ] } ], "UpdateTime": number } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. Response Elements API Version 2016-02-06 75 API Reference Application Auto Scaling CapacityForecast The capacity forecast. Type: CapacityForecast object LoadForecast The load forecast. Type: Array of LoadForecast objects UpdateTime The time the forecast was made. Type: Timestamp Errors For information about the errors that are common to all actions, see Common Errors. InternalServiceException The service encountered an internal error. HTTP Status Code: 400 ValidationException An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET Errors API Version 2016-02-06 76 API Reference Application Auto Scaling • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 77 Application Auto Scaling API Reference ListTagsForResource
app-as-api-021	app-as-api.pdf	21	HTTP Status Code: 400 See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET Errors API Version 2016-02-06 76 API Reference Application Auto Scaling • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 77 Application Auto Scaling API Reference ListTagsForResource Returns all the tags on the speciﬁed Application Auto Scaling scalable target. For general information about tags, including the format and syntax, see Tagging your AWS resources in the Amazon Web Services General Reference. Request Syntax { "ResourceARN": "string" } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. ResourceARN Specify the ARN of the scalable target. For example: arn:aws:application-autoscaling:us- east-1:123456789012:scalable-target/1234abcd56ab78cd901ef1234567890ab123 To get the ARN for a scalable target, use DescribeScalableTargets. Type: String Length Constraints: Minimum length of 1. Maximum length of 1011. Pattern: ^arn:.+:application-autoscaling:.+:[0-9]+:scalable-target\/[a-zA- Z0-9-]+$ Required: Yes Response Syntax { "Tags": { ListTagsForResource API Version 2016-02-06 78 Application Auto Scaling "string" : "string" } } Response Elements API Reference If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. Tags A list of tags. Each tag consists of a tag key and a tag value. Type: String to string map Key Length Constraints: Minimum length of 1. Maximum length of 128. Value Length Constraints: Minimum length of 0. Maximum length of 256. Errors For information about the errors that are common to all actions, see Common Errors. ResourceNotFoundException The speciﬁed resource doesn't exist. HTTP Status Code: 400 Examples If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a signature. For more information, see Signing AWS API requests in the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Example The following example lists the tag key names and values that are attached to the scalable target speciﬁed by its ARN. Response Elements API Version 2016-02-06 79 Application Auto Scaling Sample Request POST / HTTP/1.1 API Reference Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.ListTagsForResource X-Amz-Date: 20230206T120729Z User-Agent: aws-cli/2.0.0 Python/3.7.5 Windows/10 botocore/2.0.0dev4 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "ResourceARN": "arn:aws:application-autoscaling:us-west-2:123456789012:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: [request-id] Content-Type: application/x-amz-json-1.1 Content-Length: 47 Date: Mon, 06 February 2023 12:07:29 GMT { "Tags": { "environment": "production" } } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 See Also API Version 2016-02-06 80 Application Auto Scaling • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 API Reference See Also API Version 2016-02-06 81 Application Auto Scaling PutScalingPolicy API Reference Creates or updates a scaling policy for an Application Auto Scaling scalable target. Each scalable target is identiﬁed by a service namespace, resource ID, and scalable dimension. A scaling policy applies to the scalable target identiﬁed by those three attributes. You cannot create a scaling policy until you have registered the resource as a scalable target. Multiple scaling policies can be in force at the same time for the same scalable target. You can have one or more target tracking scaling policies, one or more step scaling policies, or both. However, there is a chance that multiple policies could conﬂict, instructing the scalable target to scale out or in at the same time. Application Auto Scaling gives precedence to the policy that provides the largest capacity for both scale out and scale in. For example, if one policy increases capacity by 3, another policy increases capacity by 200 percent, and the current capacity is 10, Application Auto Scaling uses the policy with the highest calculated capacity (200% of 10 = 20) and scales out to 30. We recommend caution, however, when using target tracking scaling policies with step scaling policies because conﬂicts between these policies can cause undesirable behavior. For example, if the step scaling policy initiates a scale-in activity before the target tracking policy is ready to scale in, the scale-in activity will not be blocked. After the scale-in activity completes, the target tracking policy could instruct the scalable target to
app-as-api-022	app-as-api.pdf	22	another policy increases capacity by 200 percent, and the current capacity is 10, Application Auto Scaling uses the policy with the highest calculated capacity (200% of 10 = 20) and scales out to 30. We recommend caution, however, when using target tracking scaling policies with step scaling policies because conﬂicts between these policies can cause undesirable behavior. For example, if the step scaling policy initiates a scale-in activity before the target tracking policy is ready to scale in, the scale-in activity will not be blocked. After the scale-in activity completes, the target tracking policy could instruct the scalable target to scale out again. For more information, see Target tracking scaling policies, Step scaling policies, and Predictive scaling policies in the Application Auto Scaling User Guide. Note If a scalable target is deregistered, the scalable target is no longer available to use scaling policies. Any scaling policies that were speciﬁed for the scalable target are deleted. Request Syntax { "PolicyName": "string", "PolicyType": "string", "PredictiveScalingPolicyConfiguration": { "MaxCapacityBreachBehavior": "string", "MaxCapacityBuffer": number, "MetricSpecifications": [ PutScalingPolicy API Version 2016-02-06 82 Application Auto Scaling { "CustomizedCapacityMetricSpecification": { "MetricDataQueries": [ API Reference { "Expression": "string", "Id": "string", "Label": "string", "MetricStat": { "Metric": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Namespace": "string" }, "Stat": "string", "Unit": "string" }, "ReturnData": boolean } ] }, "CustomizedLoadMetricSpecification": { "MetricDataQueries": [ { "Expression": "string", "Id": "string", "Label": "string", "MetricStat": { "Metric": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Namespace": "string" }, "Stat": "string", "Unit": "string" Request Syntax API Version 2016-02-06 83 Application Auto Scaling API Reference }, "ReturnData": boolean } ] }, "CustomizedScalingMetricSpecification": { "MetricDataQueries": [ { "Expression": "string", "Id": "string", "Label": "string", "MetricStat": { "Metric": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Namespace": "string" }, "Stat": "string", "Unit": "string" }, "ReturnData": boolean } ] }, "PredefinedLoadMetricSpecification": { "PredefinedMetricType": "string", "ResourceLabel": "string" }, "PredefinedMetricPairSpecification": { "PredefinedMetricType": "string", "ResourceLabel": "string" }, "PredefinedScalingMetricSpecification": { "PredefinedMetricType": "string", "ResourceLabel": "string" }, "TargetValue": number } ], Request Syntax API Version 2016-02-06 84 Application Auto Scaling API Reference "Mode": "string", "SchedulingBufferTime": number }, "ResourceId": "string", "ScalableDimension": "string", "ServiceNamespace": "string", "StepScalingPolicyConfiguration": { "AdjustmentType": "string", "Cooldown": number, "MetricAggregationType": "string", "MinAdjustmentMagnitude": number, "StepAdjustments": [ { "MetricIntervalLowerBound": number, "MetricIntervalUpperBound": number, "ScalingAdjustment": number } ] }, "TargetTrackingScalingPolicyConfiguration": { "CustomizedMetricSpecification": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Metrics": [ { "Expression": "string", "Id": "string", "Label": "string", "MetricStat": { "Metric": { "Dimensions": [ { "Name": "string", "Value": "string" } ], "MetricName": "string", "Namespace": "string" }, Request Syntax API Version 2016-02-06 85 Application Auto Scaling API Reference "Stat": "string", "Unit": "string" }, "ReturnData": boolean } ], "Namespace": "string", "Statistic": "string", "Unit": "string" }, "DisableScaleIn": boolean, "PredefinedMetricSpecification": { "PredefinedMetricType": "string", "ResourceLabel": "string" }, "ScaleInCooldown": number, "ScaleOutCooldown": number, "TargetValue": number } } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. PolicyName The name of the scaling policy. You cannot change the name of a scaling policy, but you can delete the original scaling policy and create a new scaling policy with the same settings and a diﬀerent name. Type: String Length Constraints: Minimum length of 1. Maximum length of 256. Pattern: \p{Print}+ Required: Yes PolicyType The scaling policy type. This parameter is required if you are creating a scaling policy. Request Parameters API Version 2016-02-06 86 Application Auto Scaling API Reference The following policy types are supported: TargetTrackingScaling—Not supported for Amazon EMR. StepScaling—Not supported for DynamoDB, Amazon Comprehend, Lambda, Amazon Keyspaces, Amazon MSK, Amazon ElastiCache, or Neptune. PredictiveScaling—Only supported for Amazon ECS. For more information, see Target tracking scaling policies, Step scaling policies, and Predictive scaling policies in the Application Auto Scaling User Guide. Type: String Valid Values: StepScaling \| TargetTrackingScaling \| PredictiveScaling Required: No PredictiveScalingPolicyConﬁguration The conﬁguration of the predictive scaling policy. Type: PredictiveScalingPolicyConﬁguration object Required: No ResourceId The identiﬁer of the resource associated with the scaling policy. This string consists of the resource type and unique identiﬁer. • ECS service - The resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. Request Parameters API Version 2016-02-06 87 Application Auto Scaling API Reference • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique
app-as-api-023	app-as-api.pdf	23	the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. Request Parameters API Version 2016-02-06 87 Application Auto Scaling API Reference • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:document-classifier-endpoint/EXAMPLE. • Amazon Comprehend entity recognizer endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:entity-recognizer-endpoint/EXAMPLE. • Lambda provisioned concurrency - The resource type is function and the unique identiﬁer is the function name with a function version or alias name suﬃx that is not $LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. Request Parameters API Version 2016-02-06 88 Application Auto Scaling API Reference • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes ScalableDimension The scalable dimension. This string consists of the service namespace, resource type, and scaling property. • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. Request Parameters API Version 2016-02-06 89 Application Auto Scaling API Reference • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for
app-as-api-024	app-as-api.pdf	24	GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- Request Parameters API Version 2016-02-06 90 Application Auto Scaling API Reference resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: Yes ServiceNamespace The namespace of the AWS service that provides the resource. For a resource provided by your own application or service, use custom-resource instead. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes StepScalingPolicyConﬁguration A step scaling policy. This parameter is required if you are creating a policy and the policy type is StepScaling. Type: StepScalingPolicyConﬁguration object Required: No TargetTrackingScalingPolicyConﬁguration A target tracking scaling policy. Includes support for predeﬁned or customized metrics. This parameter is required if you are creating a policy and the policy type is TargetTrackingScaling. Request Parameters API Version 2016-02-06 91 Application Auto Scaling API Reference Type: TargetTrackingScalingPolicyConﬁguration object Required: No Response Syntax { "Alarms": [ { "AlarmARN": "string", "AlarmName": "string" } ], "PolicyARN": "string" } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. Alarms The CloudWatch alarms created for the target tracking scaling policy. Type: Array of Alarm objects PolicyARN The Amazon Resource Name (ARN) of the resulting scaling policy. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Errors For information about the errors that are common to all actions, see Common Errors. Response Syntax API Version 2016-02-06 92 Application Auto Scaling ConcurrentUpdateException API Reference Concurrent updates caused an exception, for example, if you request an update to an Application Auto Scaling resource that already has a pending update. HTTP Status Code: 400 FailedResourceAccessException Failed access to resources caused an exception. This exception is thrown when Application Auto Scaling is unable to retrieve the alarms associated with a scaling policy due to a client error, for example, if the role ARN speciﬁed for a scalable target does not have permission to call the CloudWatch DescribeAlarms on your behalf. HTTP Status Code: 400 InternalServiceException The service encountered an internal error. HTTP Status Code: 400 LimitExceededException A per-account resource limit is exceeded. For more information, see Application Auto Scaling service quotas. HTTP Status Code: 400 ObjectNotFoundException The speciﬁed object could not be found. For any operation that depends on the existence of a scalable target, this exception is thrown if the scalable target with the speciﬁed service namespace, resource ID, and scalable dimension does not exist. For any operation that deletes or deregisters a resource, this exception is thrown if the resource cannot be found. HTTP Status Code: 400 ValidationException An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 Errors API Version 2016-02-06 93 Application Auto Scaling Examples API Reference If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a signature. For more information, see Signing AWS API requests in the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Example of a target tracking scaling policy The following example applies a target tracking scaling policy to an Amazon ECS service called web-app in the default cluster. The policy keeps the average CPU utilization of the service at 75 percent, with scale-out and scale-in cooldown periods of 60 seconds. The output contains the ARNs and names of the two CloudWatch alarms created on your behalf. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.PutScalingPolicy X-Amz-Date: 20190506T191044Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "PolicyName": "cpu75-target-tracking-scaling-policy", "PolicyType": "TargetTrackingScaling", "TargetTrackingScalingPolicyConfiguration": { "TargetValue": 75.0, "PredefinedMetricSpecification": { "PredefinedMetricType": "ECSServiceAverageCPUUtilization" }, "ScaleOutCooldown": 60, "ScaleInCooldown": 60 }, "ServiceNamespace": "ecs", "ScalableDimension": "ecs:service:DesiredCount", "ResourceId": "service/my-cluster/my-service" } Examples API Version
app-as-api-025	app-as-api.pdf	25	Amazon ECS service called web-app in the default cluster. The policy keeps the average CPU utilization of the service at 75 percent, with scale-out and scale-in cooldown periods of 60 seconds. The output contains the ARNs and names of the two CloudWatch alarms created on your behalf. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.PutScalingPolicy X-Amz-Date: 20190506T191044Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "PolicyName": "cpu75-target-tracking-scaling-policy", "PolicyType": "TargetTrackingScaling", "TargetTrackingScalingPolicyConfiguration": { "TargetValue": 75.0, "PredefinedMetricSpecification": { "PredefinedMetricType": "ECSServiceAverageCPUUtilization" }, "ScaleOutCooldown": 60, "ScaleInCooldown": 60 }, "ServiceNamespace": "ecs", "ScalableDimension": "ecs:service:DesiredCount", "ResourceId": "service/my-cluster/my-service" } Examples API Version 2016-02-06 94 Application Auto Scaling Sample Response HTTP/1.1 200 OK x-amzn-RequestId: [request-id] Content-Type: application/x-amz-json-1.1 Content-Length: 314 Date: Fri, 06 May 2019 19:10:44 GMT { API Reference "PolicyARN": "arn:aws:autoscaling:us-west-2:012345678910:scalingPolicy:6d8972f3- efc8-437c-92d1-6270f29a66e7:resource/ecs/service/my-cluster/my-service:policyName/ cpu75-target-tracking-scaling-policy", "Alarms": [ { "AlarmARN": "arn:aws:cloudwatch:us- west-2:012345678910:alarm:TargetTracking-service/my-cluster/my-service-AlarmHigh- d4f0770c-b46e-434a-a60f-3b36d653feca", "AlarmName": "TargetTracking-service/my-cluster/my-service-AlarmHigh- d4f0770c-b46e-434a-a60f-3b36d653feca" }, { "AlarmARN": "arn:aws:cloudwatch:us- west-2:012345678910:alarm:TargetTracking-service/my-cluster/my-service- AlarmLow-1b437334-d19b-4a63-a812-6c67aaf2910d", "AlarmName": "TargetTracking-service/my-cluster/my-service- AlarmLow-1b437334-d19b-4a63-a812-6c67aaf2910d" } ] } Example of a step scaling policy for scale out The following example applies a step scaling policy to an Amazon ECS service called web-app in the default cluster. The policy increases the desired count of the service by 200%, with a cooldown period of 60 seconds. The output includes the ARN for the policy, which you use to create the CloudWatch alarm. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] Examples API Version 2016-02-06 95 Application Auto Scaling API Reference X-Amz-Target: AnyScaleFrontendService.PutScalingPolicy X-Amz-Date: 20190506T191138Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "PolicyName": "my-scale-out-policy", "PolicyType": "StepScaling", "StepScalingPolicyConfiguration": { "AdjustmentType": "PercentChangeInCapacity", "Cooldown": 60, "MetricAggregationType": "Average", "StepAdjustments": [ { "ScalingAdjustment": 200, "MetricIntervalLowerBound": 0 } ] }, "ServiceNamespace": "ecs", "ScalableDimension": "ecs:service:DesiredCount", "ResourceId": "service/my-cluster/my-service" } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: [request-id] Content-Type: application/x-amz-json-1.1 Content-Length: 175 Date: Fri, 06 May 2019 19:11:38 GMT { "PolicyARN": "arn:aws:autoscaling:us-west-2:012345678910:scalingPolicy:ac542982- cbeb-4294-891c-a5a941dfa787:resource/ecs/service/my-cluster/my-service:policyName/my- scale-out-policy" } Example of a step scaling policy for scale in The following example applies a step scaling policy to the same Amazon ECS service as in the preceding example. The policy has two step adjustments that decrease the desired count of the Examples API Version 2016-02-06 96 Application Auto Scaling API Reference service by 25% or 50%, depending on the size of the alarm breach, with a cooldown period of 120 seconds. The output includes the ARN for the policy, which you use to create the CloudWatch alarm. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.PutScalingPolicy X-Amz-Date: 20190506T191152Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "PolicyName": "my-scale-in-policy", "PolicyType": "StepScaling", "StepScalingPolicyConfiguration": { "AdjustmentType": "PercentChangeInCapacity", "Cooldown": 120, "MetricAggregationType": "Average", "MinAdjustmentMagnitude": 1, "StepAdjustments": [ { "ScalingAdjustment": -25, "MetricIntervalLowerBound": -15, "MetricIntervalUpperBound": 0 }, { "ScalingAdjustment": -50, "MetricIntervalUpperBound": -15 } ] }, "ServiceNamespace": "ecs", "ScalableDimension": "ecs:service:DesiredCount", "ResourceId": "service/my-cluster/my-service" } Examples API Version 2016-02-06 97 Application Auto Scaling Sample Response HTTP/1.1 200 OK x-amzn-RequestId: [request-id] Content-Type: application/x-amz-json-1.1 Content-Length: 174 Date: Fri, 06 May 2019 19:11:52 GMT { API Reference "PolicyARN": "arn:aws:autoscaling:us-west-2:012345678910:scalingPolicy:6d8972f3- efc8-437c-92d1-6270f29a66e7:resource/ecs/service/my-cluster/my-service:policyName/my- scale-in-policy" } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 98 Application Auto Scaling API Reference PutScheduledAction Creates or updates a scheduled action for an Application Auto Scaling scalable target. Each scalable target is identiﬁed by a service namespace, resource ID, and scalable dimension. A scheduled action applies to the scalable target identiﬁed by those three attributes. You cannot create a scheduled action until you have registered the resource as a scalable target. When you specify start and end times with a recurring schedule using a cron expression or rates, they form the boundaries for when the recurring action starts and stops. To update a scheduled action, specify the parameters that you want to change. If you don't specify start and end times, the old values are deleted. For more information, see Scheduled scaling in the Application Auto Scaling User Guide. Note If a scalable target is deregistered, the scalable target is no longer available to run scheduled actions. Any scheduled actions that were speciﬁed for the scalable target are deleted. Request Syntax { "EndTime": number, "ResourceId": "string", "ScalableDimension": "string", "ScalableTargetAction": { "MaxCapacity": number, "MinCapacity": number }, "Schedule": "string", "ScheduledActionName": "string", "ServiceNamespace": "string", "StartTime": number, "Timezone": "string" } PutScheduledAction API Version 2016-02-06 99 Application Auto Scaling Request Parameters API Reference For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EndTime The date and time for the recurring schedule to end,
app-as-api-026	app-as-api.pdf	26	scalable target is deregistered, the scalable target is no longer available to run scheduled actions. Any scheduled actions that were speciﬁed for the scalable target are deleted. Request Syntax { "EndTime": number, "ResourceId": "string", "ScalableDimension": "string", "ScalableTargetAction": { "MaxCapacity": number, "MinCapacity": number }, "Schedule": "string", "ScheduledActionName": "string", "ServiceNamespace": "string", "StartTime": number, "Timezone": "string" } PutScheduledAction API Version 2016-02-06 99 Application Auto Scaling Request Parameters API Reference For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. EndTime The date and time for the recurring schedule to end, in UTC. Type: Timestamp Required: No ResourceId The identiﬁer of the resource associated with the scheduled action. This string consists of the resource type and unique identiﬁer. • ECS service - The resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The Request Parameters API Version 2016-02-06 100 Application Auto Scaling API Reference unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:document-classifier-endpoint/EXAMPLE. • Amazon Comprehend entity recognizer endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:entity-recognizer-endpoint/EXAMPLE. • Lambda provisioned concurrency - The resource type is function and the unique identiﬁer is the function name with a function version or alias name suﬃx that is not $LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Request Parameters API Version 2016-02-06 101 Application Auto Scaling Required: Yes ScalableDimension API Reference The scalable dimension. This string consists of the service namespace, resource type, and scaling property. • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for
app-as-api-027	app-as-api.pdf	27	The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. Request Parameters API Version 2016-02-06 102 Application Auto Scaling API Reference • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency Request Parameters API Version 2016-02-06 103 Application Auto Scaling API Reference \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: Yes ScalableTargetAction The new minimum and maximum capacity. You can set both values or just one. At the scheduled time, if the current capacity is below the minimum capacity, Application Auto Scaling scales out to the minimum capacity. If the current capacity is above the maximum capacity, Application Auto Scaling scales in to the maximum capacity. Type: ScalableTargetAction object Required: No Schedule The schedule for this action. The following formats are supported: • At expressions - "at(yyyy-mm-ddThh:mm:ss)" • Rate expressions - "rate(value unit)" • Cron expressions - "cron(fields)" At expressions are useful for one-time schedules. Cron expressions are useful for scheduled actions that run periodically at a speciﬁed date and time, and rate expressions are useful for scheduled actions that run at a regular interval. At and cron expressions use Universal Coordinated Time (UTC) by default. The cron format consists of six ﬁelds separated by white spaces: [Minutes] [Hours] [Day_of_Month] [Month] [Day_of_Week] [Year]. For rate expressions, value is a positive integer and unit is minute \| minutes \| hour \| hours \| day \| days. For more information, see Schedule recurring scaling actions using cron expressions in the Application Auto Scaling User Guide. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Request Parameters API Version 2016-02-06 104 Application Auto Scaling Required: No ScheduledActionName API Reference The name of the scheduled action. This name must be unique among all other scheduled actions on the speciﬁed scalable target. Type: String Length Constraints: Minimum length of 1. Maximum length of 256. Pattern: (?!((^[ ]+.)\|(.([\u0000-\u001f]\|[\u007f-\u009f]\|[:/\|])+.)\| (.[ ]+$))).+ Required: Yes ServiceNamespace The namespace of the AWS service that provides the resource. For a resource provided by your own application or service, use custom-resource instead. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes StartTime The date and time for this scheduled action to start, in UTC. Type: Timestamp Required: No Timezone Speciﬁes the time zone used when setting a scheduled action by using an at or cron expression. If a time zone is not provided, UTC is used by default. Valid values are the canonical names of the IANA time zones supported by Joda-Time (such as Etc/GMT+9 or Pacific/Tahiti). For more information, see https://www.joda.org/joda-time/ timezones.html. Request Parameters API Version 2016-02-06 105 Application Auto Scaling Type: String API Reference Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No Response Elements If the action is successful, the service sends back an HTTP 200 response with an empty HTTP body. Errors For information about the errors that are common to all actions, see Common Errors. ConcurrentUpdateException Concurrent updates caused an exception, for example, if you request an update to
app-as-api-028	app-as-api.pdf	28	values are the canonical names of the IANA time zones supported by Joda-Time (such as Etc/GMT+9 or Pacific/Tahiti). For more information, see https://www.joda.org/joda-time/ timezones.html. Request Parameters API Version 2016-02-06 105 Application Auto Scaling Type: String API Reference Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No Response Elements If the action is successful, the service sends back an HTTP 200 response with an empty HTTP body. Errors For information about the errors that are common to all actions, see Common Errors. ConcurrentUpdateException Concurrent updates caused an exception, for example, if you request an update to an Application Auto Scaling resource that already has a pending update. HTTP Status Code: 400 InternalServiceException The service encountered an internal error. HTTP Status Code: 400 LimitExceededException A per-account resource limit is exceeded. For more information, see Application Auto Scaling service quotas. HTTP Status Code: 400 ObjectNotFoundException The speciﬁed object could not be found. For any operation that depends on the existence of a scalable target, this exception is thrown if the scalable target with the speciﬁed service namespace, resource ID, and scalable dimension does not exist. For any operation that deletes or deregisters a resource, this exception is thrown if the resource cannot be found. HTTP Status Code: 400 Response Elements API Version 2016-02-06 106 Application Auto Scaling ValidationException API Reference An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 Examples If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a signature. For more information, see Signing AWS API requests in the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Example of a scheduled action This example adds a scheduled action to a DynamoDB table called TestTable to scale out on a recurring schedule. On the speciﬁed schedule (every day at 12:15pm UTC), if the current capacity is below the value speciﬁed for MinCapacity, Application Auto Scaling scales out to the value speciﬁed by MinCapacity. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.PutScheduledAction X-Amz-Date: 20190506T191138Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "ScheduledActionName": "my-recurring-action", "Schedule": "cron(15 12 * * ? *)", "ScalableTargetAction": { "MinCapacity": 6 }, "ServiceNamespace": "dynamodb", "ScalableDimension": "dynamodb:table:WriteCapacityUnits", Examples API Version 2016-02-06 107 Application Auto Scaling API Reference "ResourceId": "table/TestTable" } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 108 Application Auto Scaling API Reference RegisterScalableTarget Registers or updates a scalable target, which is the resource that you want to scale. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace, which represents some capacity dimension of the underlying service. When you register a new scalable target, you must specify values for the minimum and maximum capacity. If the speciﬁed resource is not active in the target service, this operation does not change the resource's current capacity. Otherwise, it changes the resource's current capacity to a value that is inside of this range. If you add a scaling policy, current capacity is adjustable within the speciﬁed range when scaling starts. Application Auto Scaling scaling policies will not scale capacity to values that are outside of the minimum and maximum range. After you register a scalable target, you do not need to register it again to use other Application Auto Scaling operations. To see which resources have been registered, use DescribeScalableTargets. You can also view the scaling policies for a service namespace by using DescribeScalableTargets. If you no longer need a scalable target, you can deregister it by using DeregisterScalableTarget. To update a scalable target, specify the parameters that you want to change. Include the parameters that identify the scalable target: resource ID, scalable dimension, and namespace. Any parameters that you don't specify are not changed by this update request. Note If you call the RegisterScalableTarget API operation to create a scalable target, there might be a brief delay until the operation achieves eventual consistency. You might become aware of this brief delay if you get unexpected errors when performing sequential operations. The typical strategy is to retry the request, and some AWS SDKs include automatic backoﬀ and retry logic. If you call the RegisterScalableTarget API operation to update an existing scalable target, Application Auto Scaling retrieves
app-as-api-029	app-as-api.pdf	29	identify the scalable target: resource ID, scalable dimension, and namespace. Any parameters that you don't specify are not changed by this update request. Note If you call the RegisterScalableTarget API operation to create a scalable target, there might be a brief delay until the operation achieves eventual consistency. You might become aware of this brief delay if you get unexpected errors when performing sequential operations. The typical strategy is to retry the request, and some AWS SDKs include automatic backoﬀ and retry logic. If you call the RegisterScalableTarget API operation to update an existing scalable target, Application Auto Scaling retrieves the current capacity of the resource. If it's below the minimum capacity or above the maximum capacity, Application Auto Scaling adjusts the capacity of the scalable target to place it within these bounds, even if you don't include the MinCapacity or MaxCapacity request parameters. RegisterScalableTarget API Version 2016-02-06 109 API Reference Application Auto Scaling Request Syntax { "MaxCapacity": number, "MinCapacity": number, "ResourceId": "string", "RoleARN": "string", "ScalableDimension": "string", "ServiceNamespace": "string", "SuspendedState": { "DynamicScalingInSuspended": boolean, "DynamicScalingOutSuspended": boolean, "ScheduledScalingSuspended": boolean }, "Tags": { "string" : "string" } } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. MaxCapacity The maximum value that you plan to scale out to. When a scaling policy is in eﬀect, Application Auto Scaling can scale out (expand) as needed to the maximum capacity limit in response to changing demand. This property is required when registering a new scalable target. Although you can specify a large maximum capacity, note that service quotas might impose lower limits. Each service has its own default quotas for the maximum capacity of the resource. If you want to specify a higher limit, you can request an increase. For more information, consult the documentation for that service. For information about the default quotas for each service, see Service endpoints and quotas in the Amazon Web Services General Reference. Type: Integer Required: No Request Syntax API Version 2016-02-06 110 Application Auto Scaling MinCapacity API Reference The minimum value that you plan to scale in to. When a scaling policy is in eﬀect, Application Auto Scaling can scale in (contract) as needed to the minimum capacity limit in response to changing demand. This property is required when registering a new scalable target. For the following resources, the minimum value allowed is 0. • AppStream 2.0 ﬂeets • Aurora DB clusters • ECS services • EMR clusters • Lambda provisioned concurrency • SageMaker endpoint variants • SageMaker inference components • SageMaker serverless endpoint provisioned concurrency • Spot Fleets • custom resources It's strongly recommended that you specify a value greater than 0. A value greater than 0 means that data points are continuously reported to CloudWatch that scaling policies can use to scale on a metric like average CPU utilization. For all other resources, the minimum allowed value depends on the type of resource that you are using. If you provide a value that is lower than what a resource can accept, an error occurs. In which case, the error message will provide the minimum value that the resource can accept. Type: Integer Required: No ResourceId The identiﬁer of the resource that is associated with the scalable target. This string consists of the resource type and unique identiﬁer. • ECS service - The resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. Request Parameters API Version 2016-02-06 111 Application Auto Scaling API Reference • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us-
app-as-api-030	app-as-api.pdf	30	- The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:document-classifier-endpoint/EXAMPLE. • Amazon Comprehend entity recognizer endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:entity-recognizer-endpoint/EXAMPLE. • Lambda provisioned concurrency - The resource type is function and the unique identiﬁer is the function name with a function version or alias name suﬃx that is not $LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. Request Parameters API Version 2016-02-06 112 Application Auto Scaling API Reference • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes RoleARN This parameter is required for services that do not support service-linked roles (such as Amazon EMR), and it must specify the ARN of an IAM role that allows Application Auto Scaling to modify the scalable target on your behalf. If the service supports service-linked roles, Application Auto Scaling uses a service-linked role, which it creates if it does not yet exist. For more information, see How Application Auto Scaling works with IAM. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No Request Parameters API Version 2016-02-06 113 Application Auto Scaling ScalableDimension API Reference The scalable dimension associated with the scalable target. This string consists of the service namespace, resource type, and scaling property. • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. Request Parameters API Version 2016-02-06 114 Application Auto Scaling API Reference • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type:
app-as-api-031	app-as-api.pdf	31	MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Request Parameters API Version 2016-02-06 115 Application Auto Scaling Required: Yes ServiceNamespace API Reference The namespace of the AWS service that provides the resource. For a resource provided by your own application or service, use custom-resource instead. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes SuspendedState An embedded object that contains attributes and attribute values that are used to suspend and resume automatic scaling. Setting the value of an attribute to true suspends the speciﬁed scaling activities. Setting it to false (default) resumes the speciﬁed scaling activities. Suspension Outcomes • For DynamicScalingInSuspended, while a suspension is in eﬀect, all scale-in activities that are triggered by a scaling policy are suspended. • For DynamicScalingOutSuspended, while a suspension is in eﬀect, all scale-out activities that are triggered by a scaling policy are suspended. • For ScheduledScalingSuspended, while a suspension is in eﬀect, all scaling activities that involve scheduled actions are suspended. For more information, see Suspend and resume scaling in the Application Auto Scaling User Guide. Type: SuspendedState object Required: No Tags Assigns one or more tags to the scalable target. Use this parameter to tag the scalable target when it is created. To tag an existing scalable target, use the TagResource operation. Each tag consists of a tag key and a tag value. Both the tag key and the tag value are required. You cannot have more than one tag on a scalable target with the same tag key. Request Parameters API Version 2016-02-06 116 Application Auto Scaling API Reference Use tags to control access to a scalable target. For more information, see Tagging support for Application Auto Scaling in the Application Auto Scaling User Guide. Type: String to string map Key Length Constraints: Minimum length of 1. Maximum length of 128. Value Length Constraints: Minimum length of 0. Maximum length of 256. Required: No Response Syntax { "ScalableTargetARN": "string" } Response Elements If the action is successful, the service sends back an HTTP 200 response. The following data is returned in JSON format by the service. ScalableTargetARN The ARN of the scalable target. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Errors For information about the errors that are common to all actions, see Common Errors. ConcurrentUpdateException Concurrent updates caused an exception, for example, if you request an update to an Application Auto Scaling resource that already has a pending update. HTTP Status Code: 400 Response Syntax API Version 2016-02-06 117 Application Auto Scaling InternalServiceException The service encountered an internal error. HTTP Status Code: 400 LimitExceededException API Reference A per-account resource limit is exceeded. For more information, see Application Auto Scaling service quotas. HTTP Status Code: 400 ValidationException An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 Examples If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a signature. For more information, see Signing AWS API requests in the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Example The following example registers an Amazon ECS service with Application Auto Scaling. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.RegisterScalableTarget X-Amz-Date: 20190506T182145Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS Examples API Version 2016-02-06 118 Application Auto Scaling API Reference { "ScalableDimension": "ecs:service:DesiredCount", "ResourceId": "service/my-cluster/my-service", "ServiceNamespace": "ecs", "MinCapacity": 1, "MaxCapacity": 10, "Tags": { "environment" : "production" } } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: [request-id] Content-Type: application/x-amz-json-1.1 Content-Length: 142 Date: Fri, 20 Mar 2023 12:10:44 GMT { "ScalableTargetARN": "arn:aws:application-autoscaling:us- east-1:123456789012:scalable-target/1234abcd56ab78cd901ef1234567890ab123" } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: •
app-as-api-032	app-as-api.pdf	32	service with Application Auto Scaling. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.RegisterScalableTarget X-Amz-Date: 20190506T182145Z User-Agent: aws-cli/1.10.23 Python/2.7.11 Darwin/15.4.0 botocore/1.4.8 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS Examples API Version 2016-02-06 118 Application Auto Scaling API Reference { "ScalableDimension": "ecs:service:DesiredCount", "ResourceId": "service/my-cluster/my-service", "ServiceNamespace": "ecs", "MinCapacity": 1, "MaxCapacity": 10, "Tags": { "environment" : "production" } } Sample Response HTTP/1.1 200 OK x-amzn-RequestId: [request-id] Content-Type: application/x-amz-json-1.1 Content-Length: 142 Date: Fri, 20 Mar 2023 12:10:44 GMT { "ScalableTargetARN": "arn:aws:application-autoscaling:us- east-1:123456789012:scalable-target/1234abcd56ab78cd901ef1234567890ab123" } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python See Also API Version 2016-02-06 119 Application Auto Scaling • AWS SDK for Ruby V3 API Reference See Also API Version 2016-02-06 120 Application Auto Scaling TagResource API Reference Adds or edits tags on an Application Auto Scaling scalable target. Each tag consists of a tag key and a tag value, which are both case-sensitive strings. To add a tag, specify a new tag key and a tag value. To edit a tag, specify an existing tag key and a new tag value. You can use this operation to tag an Application Auto Scaling scalable target, but you cannot tag a scaling policy or scheduled action. You can also add tags to an Application Auto Scaling scalable target while creating it (RegisterScalableTarget). For general information about tags, including the format and syntax, see Tagging your AWS resources in the Amazon Web Services General Reference. Use tags to control access to a scalable target. For more information, see Tagging support for Application Auto Scaling in the Application Auto Scaling User Guide. Request Syntax { "ResourceARN": "string", "Tags": { "string" : "string" } } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. ResourceARN Identiﬁes the Application Auto Scaling scalable target that you want to apply tags to. For example: arn:aws:application-autoscaling:us- east-1:123456789012:scalable-target/1234abcd56ab78cd901ef1234567890ab123 TagResource API Version 2016-02-06 121 Application Auto Scaling API Reference To get the ARN for a scalable target, use DescribeScalableTargets. Type: String Length Constraints: Minimum length of 1. Maximum length of 1011. Pattern: ^arn:.+:application-autoscaling:.+:[0-9]+:scalable-target\/[a-zA- Z0-9-]+$ Required: Yes Tags The tags assigned to the resource. A tag is a label that you assign to an AWS resource. Each tag consists of a tag key and a tag value. You cannot have more than one tag on an Application Auto Scaling scalable target with the same tag key. If you specify an existing tag key with a diﬀerent tag value, Application Auto Scaling replaces the current tag value with the speciﬁed one. For information about the rules that apply to tag keys and tag values, see User-deﬁned tag restrictions in the AWS Billing User Guide. Type: String to string map Key Length Constraints: Minimum length of 1. Maximum length of 128. Value Length Constraints: Minimum length of 0. Maximum length of 256. Required: Yes Response Elements If the action is successful, the service sends back an HTTP 200 response with an empty HTTP body. Errors For information about the errors that are common to all actions, see Common Errors. ResourceNotFoundException The speciﬁed resource doesn't exist. Response Elements API Version 2016-02-06 122 Application Auto Scaling HTTP Status Code: 400 TooManyTagsException API Reference The request contains too many tags. Try the request again with fewer tags. HTTP Status Code: 400 ValidationException An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 Examples If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a signature. For more information, see Signing AWS API requests in the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Example The following example adds a tag with the key name "environment" and the value "production" to the scalable target speciﬁed by its ARN. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.TagResource X-Amz-Date: 20230506T182145Z User-Agent: aws-cli/2.0.0 Python/3.7.5 Windows/10 botocore/2.0.0dev4 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "ResourceARN": "arn:aws:application-autoscaling:us-west-2:123456789012:scalable- target/1234abcd56ab78cd901ef1234567890ab123", "Tags": { Examples API Version 2016-02-06 123 Application Auto Scaling API Reference "environment": "production" } } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java
app-as-api-033	app-as-api.pdf	33	the value "production" to the scalable target speciﬁed by its ARN. Sample Request POST / HTTP/1.1 Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.TagResource X-Amz-Date: 20230506T182145Z User-Agent: aws-cli/2.0.0 Python/3.7.5 Windows/10 botocore/2.0.0dev4 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "ResourceARN": "arn:aws:application-autoscaling:us-west-2:123456789012:scalable- target/1234abcd56ab78cd901ef1234567890ab123", "Tags": { Examples API Version 2016-02-06 123 Application Auto Scaling API Reference "environment": "production" } } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 124 Application Auto Scaling UntagResource API Reference Deletes tags from an Application Auto Scaling scalable target. To delete a tag, specify the tag key and the Application Auto Scaling scalable target. Request Syntax { "ResourceARN": "string", "TagKeys": [ "string" ] } Request Parameters For information about the parameters that are common to all actions, see Common Parameters. The request accepts the following data in JSON format. ResourceARN Identiﬁes the Application Auto Scaling scalable target from which to remove tags. For example: arn:aws:application-autoscaling:us- east-1:123456789012:scalable-target/1234abcd56ab78cd901ef1234567890ab123 To get the ARN for a scalable target, use DescribeScalableTargets. Type: String Length Constraints: Minimum length of 1. Maximum length of 1011. Pattern: ^arn:.+:application-autoscaling:.+:[0-9]+:scalable-target\/[a-zA- Z0-9-]+$ Required: Yes TagKeys One or more tag keys. Specify only the tag keys, not the tag values. Type: Array of strings Array Members: Minimum number of 0 items. Maximum number of 200 items. UntagResource API Version 2016-02-06 125 Application Auto Scaling API Reference Length Constraints: Minimum length of 1. Maximum length of 128. Required: Yes Response Elements If the action is successful, the service sends back an HTTP 200 response with an empty HTTP body. Errors For information about the errors that are common to all actions, see Common Errors. ResourceNotFoundException The speciﬁed resource doesn't exist. HTTP Status Code: 400 ValidationException An exception was thrown for a validation issue. Review the available parameters for the API request. HTTP Status Code: 400 Examples If you plan to create requests manually, you must replace the Authorization header contents in the examples (AUTHPARAMS) with a signature. For more information, see Signing AWS API requests in the IAM User Guide. If you plan to use the AWS CLI or one of the AWS SDKs, these tools sign the requests for you. Example The following example removes the tag pair with the key name "environment" from the scalable target speciﬁed by its ARN. Sample Request POST / HTTP/1.1 Response Elements API Version 2016-02-06 126 Application Auto Scaling API Reference Host: application-autoscaling.us-west-2.amazonaws.com Accept-Encoding: identity Content-Length: [content-length] X-Amz-Target: AnyScaleFrontendService.UntagResource X-Amz-Date: 20230506T211829Z User-Agent: aws-cli/2.0.0 Python/3.7.5 Windows/10 botocore/2.0.0dev4 Content-Type: application/x-amz-json-1.1 Authorization: AUTHPARAMS { "ResourceARN": "arn:aws:application-autoscaling:us-west-2:123456789012:scalable- target/1234abcd56ab78cd901ef1234567890ab123", "TagKeys": [ "environment" ] } See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS Command Line Interface • AWS SDK for .NET • AWS SDK for C++ • AWS SDK for Go v2 • AWS SDK for Java V2 • AWS SDK for JavaScript V3 • AWS SDK for Kotlin • AWS SDK for PHP V3 • AWS SDK for Python • AWS SDK for Ruby V3 See Also API Version 2016-02-06 127 Application Auto Scaling Data Types API Reference The Application Auto Scaling API contains several data types that various actions use. This section describes each data type in detail. Note The order of each element in a data type structure is not guaranteed. Applications should not assume a particular order. The following data types are supported: • Alarm • CapacityForecast • CustomizedMetricSpeciﬁcation • LoadForecast • MetricDimension • NotScaledReason • PredeﬁnedMetricSpeciﬁcation • PredictiveScalingCustomizedMetricSpeciﬁcation • PredictiveScalingMetric • PredictiveScalingMetricDataQuery • PredictiveScalingMetricDimension • PredictiveScalingMetricSpeciﬁcation • PredictiveScalingMetricStat • PredictiveScalingPolicyConﬁguration • PredictiveScalingPredeﬁnedLoadMetricSpeciﬁcation • PredictiveScalingPredeﬁnedMetricPairSpeciﬁcation • PredictiveScalingPredeﬁnedScalingMetricSpeciﬁcation • ScalableTarget • ScalableTargetAction • ScalingActivity API Version 2016-02-06 128 API Reference Application Auto Scaling • ScalingPolicy • ScheduledAction • StepAdjustment • StepScalingPolicyConﬁguration • SuspendedState • TargetTrackingMetric • TargetTrackingMetricDataQuery • TargetTrackingMetricDimension • TargetTrackingMetricStat • TargetTrackingScalingPolicyConﬁguration API Version 2016-02-06 129 API Reference Application Auto Scaling Alarm Represents a CloudWatch alarm associated with a scaling policy. Contents AlarmARN The Amazon Resource Name (ARN) of the alarm. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes AlarmName The name of the alarm. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 Alarm API Version 2016-02-06 130 Application Auto Scaling CapacityForecast API Reference A GetPredictiveScalingForecast call returns the capacity forecast for a predictive scaling policy. This structure includes the data points
app-as-api-034	app-as-api.pdf	34	CloudWatch alarm associated with a scaling policy. Contents AlarmARN The Amazon Resource Name (ARN) of the alarm. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes AlarmName The name of the alarm. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 Alarm API Version 2016-02-06 130 Application Auto Scaling CapacityForecast API Reference A GetPredictiveScalingForecast call returns the capacity forecast for a predictive scaling policy. This structure includes the data points for that capacity forecast, along with the timestamps of those data points. Contents Timestamps The timestamps for the data points, in UTC format. Type: Array of timestamps Required: Yes Values The values of the data points. Type: Array of doubles Required: Yes See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 CapacityForecast API Version 2016-02-06 131 Application Auto Scaling API Reference CustomizedMetricSpeciﬁcation Represents a CloudWatch metric of your choosing for a target tracking scaling policy to use with Application Auto Scaling. For information about the available metrics for a service, see AWS services that publish CloudWatch metrics in the Amazon CloudWatch User Guide. To create your customized metric speciﬁcation: • Add values for each required parameter from CloudWatch. You can use an existing metric, or a new metric that you create. To use your own metric, you must ﬁrst publish the metric to CloudWatch. For more information, see Publish custom metrics in the Amazon CloudWatch User Guide. • Choose a metric that changes proportionally with capacity. The value of the metric should increase or decrease in inverse proportion to the number of capacity units. That is, the value of the metric should decrease when capacity increases, and increase when capacity decreases. For more information about the CloudWatch terminology below, see Amazon CloudWatch concepts in the Amazon CloudWatch User Guide. Contents Dimensions The dimensions of the metric. Conditional: If you published your metric with dimensions, you must specify the same dimensions in your scaling policy. Type: Array of MetricDimension objects Required: No MetricName The name of the metric. To get the exact metric name, namespace, and dimensions, inspect the Metric object that's returned by a call to ListMetrics. Type: String CustomizedMetricSpeciﬁcation API Version 2016-02-06 132 Application Auto Scaling Required: No Metrics API Reference The metrics to include in the target tracking scaling policy, as a metric data query. This can include both raw metric and metric math expressions. Type: Array of TargetTrackingMetricDataQuery objects Required: No Namespace The namespace of the metric. Type: String Required: No Statistic The statistic of the metric. Type: String Valid Values: Average \| Minimum \| Maximum \| SampleCount \| Sum Required: No Unit The unit of the metric. For a complete list of the units that CloudWatch supports, see the MetricDatum data type in the Amazon CloudWatch API Reference. Type: String Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ See Also API Version 2016-02-06 133 Application Auto Scaling • AWS SDK for Java V2 • AWS SDK for Ruby V3 API Reference See Also API Version 2016-02-06 134 Application Auto Scaling LoadForecast API Reference A GetPredictiveScalingForecast call returns the load forecast for a predictive scaling policy. This structure includes the data points for that load forecast, along with the timestamps of those data points and the metric speciﬁcation. Contents MetricSpeciﬁcation The metric speciﬁcation for the load forecast. Type: PredictiveScalingMetricSpeciﬁcation object Required: Yes Timestamps The timestamps for the data points, in UTC format. Type: Array of timestamps Required: Yes Values The values of the data points. Type: Array of doubles Required: Yes See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 LoadForecast API Version 2016-02-06 135 Application Auto Scaling MetricDimension API Reference Describes the dimension names and values associated with a metric. Contents Name The name of the dimension. Type: String Required: Yes Value The value of the dimension. Type: String Required: Yes See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 MetricDimension API Version 2016-02-06 136 Application Auto Scaling NotScaledReason API Reference Describes the reason for an activity that isn't scaled (not scaled activity), in machine-readable format. For help interpreting the not scaled reason details, see Scaling activities
app-as-api-035	app-as-api.pdf	35	and values associated with a metric. Contents Name The name of the dimension. Type: String Required: Yes Value The value of the dimension. Type: String Required: Yes See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 MetricDimension API Version 2016-02-06 136 Application Auto Scaling NotScaledReason API Reference Describes the reason for an activity that isn't scaled (not scaled activity), in machine-readable format. For help interpreting the not scaled reason details, see Scaling activities for Application Auto Scaling in the Application Auto Scaling User Guide. Contents Code A code that represents the reason for not scaling. Valid values: • AutoScalingAnticipatedFlapping • TargetServicePutResourceAsUnscalable • AlreadyAtMaxCapacity • AlreadyAtMinCapacity • AlreadyAtDesiredCapacity Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes CurrentCapacity The current capacity. Type: Integer Required: No MaxCapacity The maximum capacity. Type: Integer Required: No NotScaledReason API Version 2016-02-06 137 Application Auto Scaling MinCapacity The minimum capacity. Type: Integer Required: No See Also API Reference For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 138 Application Auto Scaling API Reference PredeﬁnedMetricSpeciﬁcation Represents a predeﬁned metric for a target tracking scaling policy to use with Application Auto Scaling. For more information, Predeﬁned metrics for target tracking scaling policies in the Application Auto Scaling User Guide. Contents PredeﬁnedMetricType The metric type. The ALBRequestCountPerTarget metric type applies only to Spot Fleets and ECS services. Type: String Valid Values: DynamoDBReadCapacityUtilization \| DynamoDBWriteCapacityUtilization \| ALBRequestCountPerTarget \| RDSReaderAverageCPUUtilization \| RDSReaderAverageDatabaseConnections \| EC2SpotFleetRequestAverageCPUUtilization \| EC2SpotFleetRequestAverageNetworkIn \| EC2SpotFleetRequestAverageNetworkOut \| SageMakerVariantInvocationsPerInstance \| ECSServiceAverageCPUUtilization \| ECSServiceAverageMemoryUtilization \| AppStreamAverageCapacityUtilization \| ComprehendInferenceUtilization \| LambdaProvisionedConcurrencyUtilization \| CassandraReadCapacityUtilization \| CassandraWriteCapacityUtilization \| KafkaBrokerStorageUtilization \| ElastiCacheEngineCPUUtilization \| ElastiCacheDatabaseMemoryUsagePercentage \| ElastiCachePrimaryEngineCPUUtilization \| ElastiCacheReplicaEngineCPUUtilization \| ElastiCacheDatabaseMemoryUsageCountedForEvictPercentage \| NeptuneReaderAverageCPUUtilization \| SageMakerVariantProvisionedConcurrencyUtilization \| ElastiCacheDatabaseCapacityUsageCountedForEvictPercentage \| SageMakerInferenceComponentInvocationsPerCopy \| WorkSpacesAverageUserSessionsCapacityUtilization \| PredeﬁnedMetricSpeciﬁcation API Version 2016-02-06 139 Application Auto Scaling API Reference SageMakerInferenceComponentConcurrentRequestsPerCopyHighResolution \| SageMakerVariantConcurrentRequestsPerModelHighResolution Required: Yes ResourceLabel Identiﬁes the resource associated with the metric type. You can't specify a resource label unless the metric type is ALBRequestCountPerTarget and there is a target group attached to the Spot Fleet or ECS service. You create the resource label by appending the ﬁnal portion of the load balancer ARN and the ﬁnal portion of the target group ARN into a single value, separated by a forward slash (/). The format of the resource label is: app/my-alb/778d41231b141a0f/targetgroup/my-alb-target- group/943f017f100becff. Where: • app/<load-balancer-name>/<load-balancer-id> is the ﬁnal portion of the load balancer ARN • targetgroup/<target-group-name>/<target-group-id> is the ﬁnal portion of the target group ARN. To ﬁnd the ARN for an Application Load Balancer, use the DescribeLoadBalancers API operation. To ﬁnd the ARN for the target group, use the DescribeTargetGroups API operation. Type: String Length Constraints: Minimum length of 1. Maximum length of 1023. Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 140 Application Auto Scaling API Reference See Also API Version 2016-02-06 141 Application Auto Scaling API Reference PredictiveScalingCustomizedMetricSpeciﬁcation Represents a CloudWatch metric of your choosing for a predictive scaling policy. Contents MetricDataQueries One or more metric data queries to provide data points for a metric speciﬁcation. Type: Array of PredictiveScalingMetricDataQuery objects Required: Yes See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 PredictiveScalingCustomizedMetricSpeciﬁcation API Version 2016-02-06 142 Application Auto Scaling API Reference PredictiveScalingMetric Describes the scaling metric. Contents Dimensions Describes the dimensions of the metric. Type: Array of PredictiveScalingMetricDimension objects Required: No MetricName The name of the metric. Type: String Length Constraints: Minimum length of 1. Maximum length of 255. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No Namespace The namespace of the metric. Type: String Length Constraints: Minimum length of 1. Maximum length of 255. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: PredictiveScalingMetric API Version 2016-02-06 143 Application Auto Scaling • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 API Reference See Also API Version 2016-02-06 144 Application Auto Scaling API Reference PredictiveScalingMetricDataQuery The metric data to return. Also deﬁnes whether this call is returning data for one metric only, or whether it is performing a math expression on the values of returned metric statistics to create a new time series. A time series is a series of data points, each of which is
app-as-api-036	app-as-api.pdf	36	in one of the language-speciﬁc AWS SDKs, see the following: PredictiveScalingMetric API Version 2016-02-06 143 Application Auto Scaling • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 API Reference See Also API Version 2016-02-06 144 Application Auto Scaling API Reference PredictiveScalingMetricDataQuery The metric data to return. Also deﬁnes whether this call is returning data for one metric only, or whether it is performing a math expression on the values of returned metric statistics to create a new time series. A time series is a series of data points, each of which is associated with a timestamp. Contents Id A short name that identiﬁes the object's results in the response. This name must be unique among all MetricDataQuery objects speciﬁed for a single scaling policy. If you are performing math expressions on this set of data, this name represents that data and can serve as a variable in the mathematical expression. The valid characters are letters, numbers, and underscores. The ﬁrst character must be a lowercase letter. Type: String Length Constraints: Minimum length of 1. Maximum length of 255. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes Expression The math expression to perform on the returned data, if this object is performing a math expression. This expression can use the Id of the other metrics to refer to those metrics, and can also use the Id of other expressions to use the result of those expressions. Conditional: Within each MetricDataQuery object, you must specify either Expression or MetricStat, but not both. Type: String Length Constraints: Minimum length of 1. Maximum length of 2048. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No PredictiveScalingMetricDataQuery API Version 2016-02-06 145 Application Auto Scaling Label API Reference A human-readable label for this metric or expression. This is especially useful if this is a math expression, so that you know what the value represents. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No MetricStat Information about the metric data to return. Conditional: Within each MetricDataQuery object, you must specify either Expression or MetricStat, but not both. Type: PredictiveScalingMetricStat object Required: No ReturnData Indicates whether to return the timestamps and raw data values of this metric. If you use any math expressions, specify true for this value for only the ﬁnal math expression that the metric speciﬁcation is based on. You must specify false for ReturnData for all the other metrics and expressions used in the metric speciﬁcation. If you are only retrieving metrics and not performing any math expressions, do not specify anything for ReturnData. This sets it to its default (true). Type: Boolean Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ See Also API Version 2016-02-06 146 Application Auto Scaling • AWS SDK for Java V2 • AWS SDK for Ruby V3 API Reference See Also API Version 2016-02-06 147 Application Auto Scaling API Reference PredictiveScalingMetricDimension Describes the dimension of a metric. Contents Name The name of the dimension. Type: String Length Constraints: Minimum length of 1. Maximum length of 255. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes Value The value of the dimension. Type: String Length Constraints: Minimum length of 1. Maximum length of 1024. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 PredictiveScalingMetricDimension API Version 2016-02-06 148 Application Auto Scaling API Reference PredictiveScalingMetricSpeciﬁcation This structure speciﬁes the metrics and target utilization settings for a predictive scaling policy. You must specify either a metric pair, or a load metric and a scaling metric individually. Specifying a metric pair instead of individual metrics provides a simpler way to conﬁgure metrics for a scaling policy. You choose the metric pair, and the policy automatically knows the correct sum and average statistics to use for the load metric and the scaling metric. Contents TargetValue Speciﬁes the target utilization. Type: Double Required: Yes CustomizedCapacityMetricSpeciﬁcation The customized capacity metric speciﬁcation. Type: PredictiveScalingCustomizedMetricSpeciﬁcation object Required: No CustomizedLoadMetricSpeciﬁcation The customized load metric speciﬁcation. Type: PredictiveScalingCustomizedMetricSpeciﬁcation object Required: No CustomizedScalingMetricSpeciﬁcation The customized scaling metric speciﬁcation. Type: PredictiveScalingCustomizedMetricSpeciﬁcation object Required: No PredeﬁnedLoadMetricSpeciﬁcation The predeﬁned load metric speciﬁcation. PredictiveScalingMetricSpeciﬁcation API Version 2016-02-06 149 Application Auto Scaling API Reference Type: PredictiveScalingPredeﬁnedLoadMetricSpeciﬁcation object Required: No PredeﬁnedMetricPairSpeciﬁcation The predeﬁned metric pair speciﬁcation that determines the appropriate scaling metric and load metric to use. Type: PredictiveScalingPredeﬁnedMetricPairSpeciﬁcation object Required: No PredeﬁnedScalingMetricSpeciﬁcation The predeﬁned scaling metric speciﬁcation. Type: PredictiveScalingPredeﬁnedScalingMetricSpeciﬁcation object Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 150 Application Auto Scaling
app-as-api-037	app-as-api.pdf	37	No PredeﬁnedLoadMetricSpeciﬁcation The predeﬁned load metric speciﬁcation. PredictiveScalingMetricSpeciﬁcation API Version 2016-02-06 149 Application Auto Scaling API Reference Type: PredictiveScalingPredeﬁnedLoadMetricSpeciﬁcation object Required: No PredeﬁnedMetricPairSpeciﬁcation The predeﬁned metric pair speciﬁcation that determines the appropriate scaling metric and load metric to use. Type: PredictiveScalingPredeﬁnedMetricPairSpeciﬁcation object Required: No PredeﬁnedScalingMetricSpeciﬁcation The predeﬁned scaling metric speciﬁcation. Type: PredictiveScalingPredeﬁnedScalingMetricSpeciﬁcation object Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 150 Application Auto Scaling API Reference PredictiveScalingMetricStat This structure deﬁnes the CloudWatch metric to return, along with the statistic and unit. Contents Metric The CloudWatch metric to return, including the metric name, namespace, and dimensions. To get the exact metric name, namespace, and dimensions, inspect the Metric object that is returned by a call to ListMetrics. Type: PredictiveScalingMetric object Required: Yes Stat The statistic to return. It can include any CloudWatch statistic or extended statistic. For a list of valid values, see the table in Statistics in the Amazon CloudWatch User Guide. The most commonly used metrics for predictive scaling are Average and Sum. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes Unit The unit to use for the returned data points. For a complete list of the units that CloudWatch supports, see the MetricDatum data type in the Amazon CloudWatch API Reference. Type: String Length Constraints: Minimum length of 1. Maximum length of 1023. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No PredictiveScalingMetricStat API Version 2016-02-06 151 Application Auto Scaling See Also API Reference For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 152 Application Auto Scaling API Reference PredictiveScalingPolicyConﬁguration Represents a predictive scaling policy conﬁguration. Predictive scaling is supported on Amazon ECS services. Contents MetricSpeciﬁcations This structure includes the metrics and target utilization to use for predictive scaling. This is an array, but we currently only support a single metric speciﬁcation. That is, you can specify a target value and a single metric pair, or a target value and one scaling metric and one load metric. Type: Array of PredictiveScalingMetricSpeciﬁcation objects Required: Yes MaxCapacityBreachBehavior Deﬁnes the behavior that should be applied if the forecast capacity approaches or exceeds the maximum capacity. Defaults to HonorMaxCapacity if not speciﬁed. Type: String Valid Values: HonorMaxCapacity \| IncreaseMaxCapacity Required: No MaxCapacityBuﬀer The size of the capacity buﬀer to use when the forecast capacity is close to or exceeds the maximum capacity. The value is speciﬁed as a percentage relative to the forecast capacity. For example, if the buﬀer is 10, this means a 10 percent buﬀer, such that if the forecast capacity is 50, and the maximum capacity is 40, then the eﬀective maximum capacity is 55. Required if the MaxCapacityBreachBehavior property is set to IncreaseMaxCapacity, and cannot be used otherwise. Type: Integer Valid Range: Minimum value of 0. Maximum value of 100. PredictiveScalingPolicyConﬁguration API Version 2016-02-06 153 Application Auto Scaling Required: No Mode API Reference The predictive scaling mode. Defaults to ForecastOnly if not speciﬁed. Type: String Valid Values: ForecastOnly \| ForecastAndScale Required: No SchedulingBuﬀerTime The amount of time, in seconds, that the start time can be advanced. The value must be less than the forecast interval duration of 3600 seconds (60 minutes). Defaults to 300 seconds if not speciﬁed. Type: Integer Valid Range: Minimum value of 0. Maximum value of 3600. Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 154 Application Auto Scaling API Reference PredictiveScalingPredeﬁnedLoadMetricSpeciﬁcation Describes a load metric for a predictive scaling policy. When returned in the output of DescribePolicies, it indicates that a predictive scaling policy uses individually speciﬁed load and scaling metrics instead of a metric pair. Contents PredeﬁnedMetricType The metric type. Type: String Length Constraints: Minimum length of 1. Maximum length of 128. Required: Yes ResourceLabel A label that uniquely identiﬁes a target group. Type: String Length Constraints: Minimum length of 1. Maximum length of 1023. Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 PredictiveScalingPredeﬁnedLoadMetricSpeciﬁcation API Version 2016-02-06 155 Application Auto Scaling API Reference PredictiveScalingPredeﬁnedMetricPairSpeciﬁcation Represents a metric pair for a predictive scaling policy. Contents PredeﬁnedMetricType Indicates which metrics to use. There are two diﬀerent types of metrics for each metric type: one is a load metric and one is
app-as-api-038	app-as-api.pdf	38	identiﬁes a target group. Type: String Length Constraints: Minimum length of 1. Maximum length of 1023. Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 PredictiveScalingPredeﬁnedLoadMetricSpeciﬁcation API Version 2016-02-06 155 Application Auto Scaling API Reference PredictiveScalingPredeﬁnedMetricPairSpeciﬁcation Represents a metric pair for a predictive scaling policy. Contents PredeﬁnedMetricType Indicates which metrics to use. There are two diﬀerent types of metrics for each metric type: one is a load metric and one is a scaling metric. Type: String Length Constraints: Minimum length of 1. Maximum length of 128. Required: Yes ResourceLabel A label that uniquely identiﬁes a speciﬁc target group from which to determine the total and average request count. Type: String Length Constraints: Minimum length of 1. Maximum length of 1023. Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 PredictiveScalingPredeﬁnedMetricPairSpeciﬁcation API Version 2016-02-06 156 Application Auto Scaling API Reference PredictiveScalingPredeﬁnedScalingMetricSpeciﬁcation Describes a scaling metric for a predictive scaling policy. When returned in the output of DescribePolicies, it indicates that a predictive scaling policy uses individually speciﬁed load and scaling metrics instead of a metric pair. Contents PredeﬁnedMetricType The metric type. Type: String Length Constraints: Minimum length of 1. Maximum length of 128. Required: Yes ResourceLabel A label that uniquely identiﬁes a speciﬁc target group from which to determine the average request count. Type: String Length Constraints: Minimum length of 1. Maximum length of 1023. Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 PredictiveScalingPredeﬁnedScalingMetricSpeciﬁcation API Version 2016-02-06 157 API Reference Application Auto Scaling ScalableTarget Represents a scalable target. Contents CreationTime The Unix timestamp for when the scalable target was created. Type: Timestamp Required: Yes MaxCapacity The maximum value to scale to in response to a scale-out activity. Type: Integer Required: Yes MinCapacity The minimum value to scale to in response to a scale-in activity. Type: Integer Required: Yes ResourceId The identiﬁer of the resource associated with the scalable target. This string consists of the resource type and unique identiﬁer. • ECS service - The resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. ScalableTarget API Version 2016-02-06 158 Application Auto Scaling API Reference • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:document-classifier-endpoint/EXAMPLE. • Amazon Comprehend entity recognizer endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:entity-recognizer-endpoint/EXAMPLE. • Lambda provisioned concurrency - The resource type is function and the unique identiﬁer is the function name with a function version or alias name suﬃx that is not $LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the
app-as-api-039	app-as-api.pdf	39	$LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. Contents API Version 2016-02-06 159 Application Auto Scaling API Reference • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes RoleARN The ARN of an IAM role that allows Application Auto Scaling to modify the scalable target on your behalf. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes ScalableDimension The scalable dimension associated with the scalable target. This string consists of the service namespace, resource type, and scaling property. • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. Contents API Version 2016-02-06 160 Application Auto Scaling API Reference • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. Contents API Version 2016-02-06 161 Application Auto Scaling API Reference • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: Yes ServiceNamespace The namespace of the AWS service that provides the resource, or a custom-resource. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes Contents API Version 2016-02-06 162 API Reference Application Auto Scaling PredictedCapacity The predicted capacity of the scalable target. Type: Integer Required: No ScalableTargetARN The ARN of the scalable target. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No SuspendedState Speciﬁes whether the scaling activities for a scalable target are in a suspended state. Type: SuspendedState object Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 163 Application Auto Scaling API Reference
app-as-api-040	app-as-api.pdf	40	Version 2016-02-06 162 API Reference Application Auto Scaling PredictedCapacity The predicted capacity of the scalable target. Type: Integer Required: No ScalableTargetARN The ARN of the scalable target. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No SuspendedState Speciﬁes whether the scaling activities for a scalable target are in a suspended state. Type: SuspendedState object Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 163 Application Auto Scaling API Reference ScalableTargetAction Represents the minimum and maximum capacity for a scheduled action. Contents MaxCapacity The maximum capacity. Although you can specify a large maximum capacity, note that service quotas may impose lower limits. Each service has its own default quotas for the maximum capacity of the resource. If you want to specify a higher limit, you can request an increase. For more information, consult the documentation for that service. For information about the default quotas for each service, see Service endpoints and quotas in the Amazon Web Services General Reference. Type: Integer Required: No MinCapacity The minimum capacity. When the scheduled action runs, the resource will have at least this much capacity, but it might have more depending on other settings, such as the target utilization level of a target tracking scaling policy. For certain resources, the minimum value allowed is 0. For more information, see RegisterScalableTarget. Type: Integer Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ ScalableTargetAction API Version 2016-02-06 164 Application Auto Scaling • AWS SDK for Java V2 • AWS SDK for Ruby V3 API Reference See Also API Version 2016-02-06 165 API Reference Application Auto Scaling ScalingActivity Represents a scaling activity. Contents ActivityId The unique identiﬁer of the scaling activity. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes Cause A simple description of what caused the scaling activity to happen. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes Description A simple description of what action the scaling activity intends to accomplish. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes ResourceId The identiﬁer of the resource associated with the scaling activity. This string consists of the resource type and unique identiﬁer. • ECS service - The resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. ScalingActivity API Version 2016-02-06 166 Application Auto Scaling API Reference • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:document-classifier-endpoint/EXAMPLE. • Amazon Comprehend entity recognizer endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:entity-recognizer-endpoint/EXAMPLE. • Lambda provisioned concurrency - The resource type is function and the unique identiﬁer is the function name with a function version or alias name suﬃx that is not $LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. Contents API Version 2016-02-06 167 Application Auto Scaling API Reference • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless
app-as-api-041	app-as-api.pdf	41	table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. Contents API Version 2016-02-06 167 Application Auto Scaling API Reference • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes ScalableDimension The scalable dimension. This string consists of the service namespace, resource type, and scaling property. • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. Contents API Version 2016-02-06 168 Application Auto Scaling API Reference • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. Contents API Version 2016-02-06 169 Application Auto Scaling API Reference • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: Yes ServiceNamespace The namespace of the AWS service that provides the resource, or a custom-resource. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes StartTime The Unix timestamp for when the scaling activity began. Type: Timestamp Contents API Version 2016-02-06 170 Application Auto Scaling Required: Yes StatusCode Indicates the status of the scaling activity. Type: String API Reference Valid Values: Pending \| InProgress \| Successful \| Overridden \| Unfulfilled \| Failed Required: Yes Details The details about the scaling activity. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No EndTime The Unix timestamp for when the scaling activity ended. Type: Timestamp Required: No NotScaledReasons Machine-readable data that describes the reason for a not scaled activity. Only available when DescribeScalingActivities includes not scaled activities. Type: Array of NotScaledReason objects Required: No StatusMessage A simple message about the current status of the scaling activity. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Contents API Version 2016-02-06 171 Application Auto Scaling Required: No See Also API Reference For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK
app-as-api-042	app-as-api.pdf	42	Unix timestamp for when the scaling activity ended. Type: Timestamp Required: No NotScaledReasons Machine-readable data that describes the reason for a not scaled activity. Only available when DescribeScalingActivities includes not scaled activities. Type: Array of NotScaledReason objects Required: No StatusMessage A simple message about the current status of the scaling activity. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Contents API Version 2016-02-06 171 Application Auto Scaling Required: No See Also API Reference For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 172 Application Auto Scaling ScalingPolicy API Reference Represents a scaling policy to use with Application Auto Scaling. For more information about conﬁguring scaling policies for a speciﬁc service, see AWS services that you can use with Application Auto Scaling in the Application Auto Scaling User Guide. Contents CreationTime The Unix timestamp for when the scaling policy was created. Type: Timestamp Required: Yes PolicyARN The Amazon Resource Name (ARN) of the scaling policy. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes PolicyName The name of the scaling policy. Type: String Length Constraints: Minimum length of 1. Maximum length of 256. Pattern: \p{Print}+ Required: Yes PolicyType The scaling policy type. ScalingPolicy API Version 2016-02-06 173 Application Auto Scaling API Reference The following policy types are supported: TargetTrackingScaling—Not supported for Amazon EMR StepScaling—Not supported for DynamoDB, Amazon Comprehend, Lambda, Amazon Keyspaces, Amazon MSK, Amazon ElastiCache, or Neptune. PredictiveScaling—Only supported for Amazon ECS Type: String Valid Values: StepScaling \| TargetTrackingScaling \| PredictiveScaling Required: Yes ResourceId The identiﬁer of the resource associated with the scaling policy. This string consists of the resource type and unique identiﬁer. • ECS service - The resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. Contents API Version 2016-02-06 174 Application Auto Scaling API Reference • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:document-classifier-endpoint/EXAMPLE. • Amazon Comprehend entity recognizer endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:entity-recognizer-endpoint/EXAMPLE. • Lambda provisioned concurrency - The resource type is function and the unique identiﬁer is the function name with a function version or alias name suﬃx that is not $LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Contents API Version 2016-02-06 175 Application Auto Scaling API Reference Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes ScalableDimension The scalable dimension. This string consists of the service namespace, resource type, and
app-as-api-043	app-as-api.pdf	43	SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Contents API Version 2016-02-06 175 Application Auto Scaling API Reference Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes ScalableDimension The scalable dimension. This string consists of the service namespace, resource type, and scaling property. • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. Contents API Version 2016-02-06 176 Application Auto Scaling API Reference • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency Contents API Version 2016-02-06 177 Application Auto Scaling API Reference \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: Yes ServiceNamespace The namespace of the AWS service that provides the resource, or a custom-resource. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes Alarms The CloudWatch alarms associated with the scaling policy. Type: Array of Alarm objects Required: No PredictiveScalingPolicyConﬁguration The predictive scaling policy conﬁguration. Type: PredictiveScalingPolicyConﬁguration object Required: No StepScalingPolicyConﬁguration A step scaling policy. Type: StepScalingPolicyConﬁguration object Required: No TargetTrackingScalingPolicyConﬁguration A target tracking scaling policy. Type: TargetTrackingScalingPolicyConﬁguration object Contents API Version 2016-02-06 178 Application Auto Scaling Required: No See Also API Reference For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 179 API Reference Application Auto Scaling ScheduledAction Represents a scheduled action. Contents CreationTime The date and time that the scheduled action was created. Type: Timestamp Required: Yes ResourceId The identiﬁer of the resource associated with the scaling policy. This string consists of the resource type and unique identiﬁer. • ECS service - The resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name.
app-as-api-044	app-as-api.pdf	44	resource type is service and the unique identiﬁer is the cluster name and service name. Example: service/my-cluster/my-service. • Spot Fleet - The resource type is spot-fleet-request and the unique identiﬁer is the Spot Fleet request ID. Example: spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE. • EMR cluster - The resource type is instancegroup and the unique identiﬁer is the cluster ID and instance group ID. Example: instancegroup/j-2EEZNYKUA1NTV/ ig-1791Y4E1L8YI0. • AppStream 2.0 ﬂeet - The resource type is fleet and the unique identiﬁer is the ﬂeet name. Example: fleet/sample-fleet. • DynamoDB table - The resource type is table and the unique identiﬁer is the table name. Example: table/my-table. • DynamoDB global secondary index - The resource type is index and the unique identiﬁer is the index name. Example: table/my-table/index/my-table-index. • Aurora DB cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:my-db-cluster. • SageMaker endpoint variant - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. ScheduledAction API Version 2016-02-06 180 Application Auto Scaling API Reference • Custom resources are not supported with a resource type. This parameter must specify the OutputValue from the CloudFormation template stack used to access the resources. The unique identiﬁer is deﬁned by the service provider. More information is available in our GitHub repository. • Amazon Comprehend document classiﬁcation endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:document-classifier-endpoint/EXAMPLE. • Amazon Comprehend entity recognizer endpoint - The resource type and unique identiﬁer are speciﬁed using the endpoint ARN. Example: arn:aws:comprehend:us- west-2:123456789012:entity-recognizer-endpoint/EXAMPLE. • Lambda provisioned concurrency - The resource type is function and the unique identiﬁer is the function name with a function version or alias name suﬃx that is not $LATEST. Example: function:my-function:prod or function:my-function:1. • Amazon Keyspaces table - The resource type is table and the unique identiﬁer is the table name. Example: keyspace/mykeyspace/table/mytable. • Amazon MSK cluster - The resource type and unique identiﬁer are speciﬁed using the cluster ARN. Example: arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5. • Amazon ElastiCache replication group - The resource type is replication-group and the unique identiﬁer is the replication group name. Example: replication-group/mycluster. • Amazon ElastiCache cache cluster - The resource type is cache-cluster and the unique identiﬁer is the cache cluster name. Example: cache-cluster/mycluster. • Neptune cluster - The resource type is cluster and the unique identiﬁer is the cluster name. Example: cluster:mycluster. • SageMaker serverless endpoint - The resource type is variant and the unique identiﬁer is the resource ID. Example: endpoint/my-end-point/variant/KMeansClustering. • SageMaker inference component - The resource type is inference-component and the unique identiﬁer is the resource ID. Example: inference-component/my-inference- component. • Pool of WorkSpaces - The resource type is workspacespool and the unique identiﬁer is the pool ID. Example: workspacespool/wspool-123456. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Contents API Version 2016-02-06 181 Application Auto Scaling API Reference Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes Schedule The schedule for this action. The following formats are supported: • At expressions - "at(yyyy-mm-ddThh:mm:ss)" • Rate expressions - "rate(value unit)" • Cron expressions - "cron(fields)" At expressions are useful for one-time schedules. Cron expressions are useful for scheduled actions that run periodically at a speciﬁed date and time, and rate expressions are useful for scheduled actions that run at a regular interval. At and cron expressions use Universal Coordinated Time (UTC) by default. The cron format consists of six ﬁelds separated by white spaces: [Minutes] [Hours] [Day_of_Month] [Month] [Day_of_Week] [Year]. For rate expressions, value is a positive integer and unit is minute \| minutes \| hour \| hours \| day \| days. For more information, see Schedule recurring scaling actions using cron expressions in the Application Auto Scaling User Guide. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes ScheduledActionARN The Amazon Resource Name (ARN) of the scheduled action. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Contents API Version 2016-02-06 182 Application Auto Scaling Required: Yes ScheduledActionName The name of the scheduled action. Type: String API Reference Length Constraints: Minimum length of 1. Maximum length of 256. Pattern: (?!((^[ ]+.)\|(.([\u0000-\u001f]\|[\u007f-\u009f]\|[:/\|])+.)\| (.[ ]+$))).+ Required: Yes ServiceNamespace The namespace of the AWS service that provides the resource, or a custom-resource. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes EndTime The date and time that the action is scheduled to end, in UTC. Type: Timestamp Required: No ScalableDimension The scalable dimension. This string consists of the service namespace, resource type, and scaling property. • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity
app-as-api-045	app-as-api.pdf	45	service that provides the resource, or a custom-resource. Type: String Valid Values: ecs \| elasticmapreduce \| ec2 \| appstream \| dynamodb \| rds \| sagemaker \| custom-resource \| comprehend \| lambda \| cassandra \| kafka \| elasticache \| neptune \| workspaces Required: Yes EndTime The date and time that the action is scheduled to end, in UTC. Type: Timestamp Required: No ScalableDimension The scalable dimension. This string consists of the service namespace, resource type, and scaling property. • ecs:service:DesiredCount - The task count of an ECS service. • elasticmapreduce:instancegroup:InstanceCount - The instance count of an EMR Instance Group. • ec2:spot-fleet-request:TargetCapacity - The target capacity of a Spot Fleet. Contents API Version 2016-02-06 183 Application Auto Scaling API Reference • appstream:fleet:DesiredCapacity - The capacity of an AppStream 2.0 ﬂeet. • dynamodb:table:ReadCapacityUnits - The provisioned read capacity for a DynamoDB table. • dynamodb:table:WriteCapacityUnits - The provisioned write capacity for a DynamoDB table. • dynamodb:index:ReadCapacityUnits - The provisioned read capacity for a DynamoDB global secondary index. • dynamodb:index:WriteCapacityUnits - The provisioned write capacity for a DynamoDB global secondary index. • rds:cluster:ReadReplicaCount - The count of Aurora Replicas in an Aurora DB cluster. Available for Aurora MySQL-compatible edition and Aurora PostgreSQL-compatible edition. • sagemaker:variant:DesiredInstanceCount - The number of EC2 instances for a SageMaker model endpoint variant. • custom-resource:ResourceType:Property - The scalable dimension for a custom resource provided by your own application or service. • comprehend:document-classifier-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend document classiﬁcation endpoint. • comprehend:entity-recognizer-endpoint:DesiredInferenceUnits - The number of inference units for an Amazon Comprehend entity recognizer endpoint. • lambda:function:ProvisionedConcurrency - The provisioned concurrency for a Lambda function. • cassandra:table:ReadCapacityUnits - The provisioned read capacity for an Amazon Keyspaces table. • cassandra:table:WriteCapacityUnits - The provisioned write capacity for an Amazon Keyspaces table. • kafka:broker-storage:VolumeSize - The provisioned volume size (in GiB) for brokers in an Amazon MSK cluster. • elasticache:cache-cluster:Nodes - The number of nodes for an Amazon ElastiCache cache cluster. • elasticache:replication-group:NodeGroups - The number of node groups for an Amazon ElastiCache replication group. • elasticache:replication-group:Replicas - The number of replicas per node group for an Amazon ElastiCache replication group. Contents API Version 2016-02-06 184 Application Auto Scaling API Reference • neptune:cluster:ReadReplicaCount - The count of read replicas in an Amazon Neptune DB cluster. • sagemaker:variant:DesiredProvisionedConcurrency - The provisioned concurrency for a SageMaker serverless endpoint. • sagemaker:inference-component:DesiredCopyCount - The number of copies across an endpoint for a SageMaker inference component. • workspaces:workspacespool:DesiredUserSessions - The number of user sessions for the WorkSpaces in the pool. Type: String Valid Values: ecs:service:DesiredCount \| ec2:spot-fleet- request:TargetCapacity \| elasticmapreduce:instancegroup:InstanceCount \| appstream:fleet:DesiredCapacity \| dynamodb:table:ReadCapacityUnits \| dynamodb:table:WriteCapacityUnits \| dynamodb:index:ReadCapacityUnits \| dynamodb:index:WriteCapacityUnits \| rds:cluster:ReadReplicaCount \| sagemaker:variant:DesiredInstanceCount \| custom- resource:ResourceType:Property \| comprehend:document-classifier- endpoint:DesiredInferenceUnits \| comprehend:entity-recognizer- endpoint:DesiredInferenceUnits \| lambda:function:ProvisionedConcurrency \| cassandra:table:ReadCapacityUnits \| cassandra:table:WriteCapacityUnits \| kafka:broker-storage:VolumeSize \| elasticache:cache-cluster:Nodes \| elasticache:replication-group:NodeGroups \| elasticache:replication- group:Replicas \| neptune:cluster:ReadReplicaCount \| sagemaker:variant:DesiredProvisionedConcurrency \| sagemaker:inference-component:DesiredCopyCount \| workspaces:workspacespool:DesiredUserSessions Required: No ScalableTargetAction The new minimum and maximum capacity. You can set both values or just one. At the scheduled time, if the current capacity is below the minimum capacity, Application Auto Scaling scales out to the minimum capacity. If the current capacity is above the maximum capacity, Application Auto Scaling scales in to the maximum capacity. Type: ScalableTargetAction object Contents API Version 2016-02-06 185 Application Auto Scaling Required: No StartTime The date and time that the action is scheduled to begin, in UTC. API Reference Type: Timestamp Required: No Timezone The time zone used when referring to the date and time of a scheduled action, when the scheduled action uses an at or cron expression. Type: String Length Constraints: Minimum length of 1. Maximum length of 1600. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 186 Application Auto Scaling StepAdjustment API Reference Represents a step adjustment for a StepScalingPolicyConﬁguration. Describes an adjustment based on the diﬀerence between the value of the aggregated CloudWatch metric and the breach threshold that you've deﬁned for the alarm. For the following examples, suppose that you have an alarm with a breach threshold of 50: • To initiate the adjustment when the metric is greater than or equal to 50 and less than 60, specify a lower bound of 0 and an upper bound of 10. • To initiate the adjustment when the metric is greater than 40 and less than or equal to 50, specify a lower bound of -10 and an upper bound of 0. There are a few rules for the step adjustments for your step policy: • The ranges of your step adjustments can't overlap or have a gap. • At most one step adjustment can have a null lower bound.
app-as-api-046	app-as-api.pdf	46	• To initiate the adjustment when the metric is greater than or equal to 50 and less than 60, specify a lower bound of 0 and an upper bound of 10. • To initiate the adjustment when the metric is greater than 40 and less than or equal to 50, specify a lower bound of -10 and an upper bound of 0. There are a few rules for the step adjustments for your step policy: • The ranges of your step adjustments can't overlap or have a gap. • At most one step adjustment can have a null lower bound. If one step adjustment has a negative lower bound, then there must be a step adjustment with a null lower bound. • At most one step adjustment can have a null upper bound. If one step adjustment has a positive upper bound, then there must be a step adjustment with a null upper bound. • The upper and lower bound can't be null in the same step adjustment. Contents ScalingAdjustment The amount by which to scale, based on the speciﬁed adjustment type. A positive value adds to the current capacity while a negative number removes from the current capacity. For exact capacity, you must specify a non-negative value. Type: Integer Required: Yes MetricIntervalLowerBound The lower bound for the diﬀerence between the alarm threshold and the CloudWatch metric. If the metric value is above the breach threshold, the lower bound is inclusive (the metric StepAdjustment API Version 2016-02-06 187 Application Auto Scaling API Reference must be greater than or equal to the threshold plus the lower bound). Otherwise, it's exclusive (the metric must be greater than the threshold plus the lower bound). A null value indicates negative inﬁnity. Type: Double Required: No MetricIntervalUpperBound The upper bound for the diﬀerence between the alarm threshold and the CloudWatch metric. If the metric value is above the breach threshold, the upper bound is exclusive (the metric must be less than the threshold plus the upper bound). Otherwise, it's inclusive (the metric must be less than or equal to the threshold plus the upper bound). A null value indicates positive inﬁnity. The upper bound must be greater than the lower bound. Type: Double Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 188 Application Auto Scaling API Reference StepScalingPolicyConﬁguration Represents a step scaling policy conﬁguration to use with Application Auto Scaling. For more information, see Step scaling policies in the Application Auto Scaling User Guide. Contents AdjustmentType Speciﬁes how the ScalingAdjustment value in a StepAdjustment is interpreted (for example, an absolute number or a percentage). The valid values are ChangeInCapacity, ExactCapacity, and PercentChangeInCapacity. AdjustmentType is required if you are adding a new step scaling policy conﬁguration. Type: String Valid Values: ChangeInCapacity \| PercentChangeInCapacity \| ExactCapacity Required: No Cooldown The amount of time, in seconds, to wait for a previous scaling activity to take eﬀect. If not speciﬁed, the default value is 300. For more information, see Cooldown period in the Application Auto Scaling User Guide. Type: Integer Required: No MetricAggregationType The aggregation type for the CloudWatch metrics. Valid values are Minimum, Maximum, and Average. If the aggregation type is null, the value is treated as Average. Type: String Valid Values: Average \| Minimum \| Maximum Required: No StepScalingPolicyConﬁguration API Version 2016-02-06 189 Application Auto Scaling MinAdjustmentMagnitude API Reference The minimum value to scale by when the adjustment type is PercentChangeInCapacity. For example, suppose that you create a step scaling policy to scale out an Amazon ECS service by 25 percent and you specify a MinAdjustmentMagnitude of 2. If the service has 4 tasks and the scaling policy is performed, 25 percent of 4 is 1. However, because you speciﬁed a MinAdjustmentMagnitude of 2, Application Auto Scaling scales out the service by 2 tasks. Type: Integer Required: No StepAdjustments A set of adjustments that enable you to scale based on the size of the alarm breach. At least one step adjustment is required if you are adding a new step scaling policy conﬁguration. Type: Array of StepAdjustment objects Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 190 Application Auto Scaling SuspendedState API Reference Speciﬁes whether the scaling activities for a scalable target are in a suspended state. Contents DynamicScalingInSuspended Whether scale in by a target tracking scaling policy or a step scaling policy is suspended. Set the value to true if you don't want Application Auto Scaling
app-as-api-047	app-as-api.pdf	47	conﬁguration. Type: Array of StepAdjustment objects Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 190 Application Auto Scaling SuspendedState API Reference Speciﬁes whether the scaling activities for a scalable target are in a suspended state. Contents DynamicScalingInSuspended Whether scale in by a target tracking scaling policy or a step scaling policy is suspended. Set the value to true if you don't want Application Auto Scaling to remove capacity when a scaling policy is triggered. The default is false. Type: Boolean Required: No DynamicScalingOutSuspended Whether scale out by a target tracking scaling policy or a step scaling policy is suspended. Set the value to true if you don't want Application Auto Scaling to add capacity when a scaling policy is triggered. The default is false. Type: Boolean Required: No ScheduledScalingSuspended Whether scheduled scaling is suspended. Set the value to true if you don't want Application Auto Scaling to add or remove capacity by initiating scheduled actions. The default is false. Type: Boolean Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 SuspendedState API Version 2016-02-06 191 Application Auto Scaling • AWS SDK for Ruby V3 API Reference See Also API Version 2016-02-06 192 Application Auto Scaling API Reference TargetTrackingMetric Represents a speciﬁc metric. Metric is a property of the TargetTrackingMetricStat object. Contents Dimensions The dimensions for the metric. For the list of available dimensions, see the AWS documentation available from the table in AWS services that publish CloudWatch metrics in the Amazon CloudWatch User Guide. Conditional: If you published your metric with dimensions, you must specify the same dimensions in your scaling policy. Type: Array of TargetTrackingMetricDimension objects Required: No MetricName The name of the metric. Type: String Length Constraints: Minimum length of 1. Maximum length of 255. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No Namespace The namespace of the metric. For more information, see the table in AWS services that publish CloudWatch metrics in the Amazon CloudWatch User Guide. Type: String Length Constraints: Minimum length of 1. Maximum length of 255. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* TargetTrackingMetric API Version 2016-02-06 193 Application Auto Scaling Required: No See Also API Reference For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 194 Application Auto Scaling API Reference TargetTrackingMetricDataQuery The metric data to return. Also deﬁnes whether this call is returning data for one metric only, or whether it is performing a math expression on the values of returned metric statistics to create a new time series. A time series is a series of data points, each of which is associated with a timestamp. You can use TargetTrackingMetricDataQuery structures with a PutScalingPolicy operation when you specify a TargetTrackingScalingPolicyConﬁguration in the request. You can call for a single metric or perform math expressions on multiple metrics. Any expressions used in a metric speciﬁcation must eventually return a single time series. For more information and examples, see Create a target tracking scaling policy for Application Auto Scaling using metric math in the Application Auto Scaling User Guide. Contents Id A short name that identiﬁes the object's results in the response. This name must be unique among all MetricDataQuery objects speciﬁed for a single scaling policy. If you are performing math expressions on this set of data, this name represents that data and can serve as a variable in the mathematical expression. The valid characters are letters, numbers, and underscores. The ﬁrst character must be a lowercase letter. Type: String Length Constraints: Minimum length of 1. Maximum length of 255. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes Expression The math expression to perform on the returned data, if this object is performing a math expression. This expression can use the Id of the other metrics to refer to those metrics, and can also use the Id of other expressions to use the result of those expressions. Conditional: Within each TargetTrackingMetricDataQuery object, you must specify either Expression or MetricStat, but not both. TargetTrackingMetricDataQuery API Version 2016-02-06 195 Application Auto Scaling Type: String API Reference Length Constraints: Minimum length of 1. Maximum length of 2048. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No Label A human-readable label for this metric or expression. This is especially useful if this is a math expression, so that you know what the value represents. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No MetricStat Information about the metric data to return. Conditional: Within each MetricDataQuery object, you must specify either
app-as-api-048	app-as-api.pdf	48	other expressions to use the result of those expressions. Conditional: Within each TargetTrackingMetricDataQuery object, you must specify either Expression or MetricStat, but not both. TargetTrackingMetricDataQuery API Version 2016-02-06 195 Application Auto Scaling Type: String API Reference Length Constraints: Minimum length of 1. Maximum length of 2048. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No Label A human-readable label for this metric or expression. This is especially useful if this is a math expression, so that you know what the value represents. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: No MetricStat Information about the metric data to return. Conditional: Within each MetricDataQuery object, you must specify either Expression or MetricStat, but not both. Type: TargetTrackingMetricStat object Required: No ReturnData Indicates whether to return the timestamps and raw data values of this metric. If you use any math expressions, specify true for this value for only the ﬁnal math expression that the metric speciﬁcation is based on. You must specify false for ReturnData for all the other metrics and expressions used in the metric speciﬁcation. If you are only retrieving metrics and not performing any math expressions, do not specify anything for ReturnData. This sets it to its default (true). Type: Boolean Required: No Contents API Version 2016-02-06 196 Application Auto Scaling See Also API Reference For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 197 Application Auto Scaling API Reference TargetTrackingMetricDimension Describes the dimension of a metric. Contents Name The name of the dimension. Type: String Length Constraints: Minimum length of 1. Maximum length of 255. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes Value The value of the dimension. Type: String Length Constraints: Minimum length of 1. Maximum length of 1024. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 TargetTrackingMetricDimension API Version 2016-02-06 198 Application Auto Scaling API Reference TargetTrackingMetricStat This structure deﬁnes the CloudWatch metric to return, along with the statistic and unit. TargetTrackingMetricStat is a property of the TargetTrackingMetricDataQuery object. For more information about the CloudWatch terminology below, see Amazon CloudWatch concepts in the Amazon CloudWatch User Guide. Contents Metric The CloudWatch metric to return, including the metric name, namespace, and dimensions. To get the exact metric name, namespace, and dimensions, inspect the Metric object that is returned by a call to ListMetrics. Type: TargetTrackingMetric object Required: Yes Stat The statistic to return. It can include any CloudWatch statistic or extended statistic. For a list of valid values, see the table in Statistics in the Amazon CloudWatch User Guide. The most commonly used metric for scaling is Average. Type: String Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* Required: Yes Unit The unit to use for the returned data points. For a complete list of the units that CloudWatch supports, see the MetricDatum data type in the Amazon CloudWatch API Reference. Type: String Length Constraints: Minimum length of 1. Maximum length of 1023. Pattern: [\u0020-\uD7FF\uE000-\uFFFD\uD800\uDC00-\uDBFF\uDFFF\r\n\t]* TargetTrackingMetricStat API Version 2016-02-06 199 Application Auto Scaling Required: No See Also API Reference For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 200 Application Auto Scaling API Reference TargetTrackingScalingPolicyConﬁguration Represents a target tracking scaling policy conﬁguration to use with Application Auto Scaling. For more information, see Target tracking scaling policies in the Application Auto Scaling User Guide. Contents TargetValue The target value for the metric. Although this property accepts numbers of type Double, it won't accept values that are either too small or too large. Values must be in the range of -2^360 to 2^360. The value must be a valid number based on the choice of metric. For example, if the metric is CPU utilization, then the target value is a percent value that represents how much of the CPU can be used before scaling out. Note If the scaling policy speciﬁes the ALBRequestCountPerTarget predeﬁned metric, specify the target utilization as the optimal average request count per target during any one-minute interval. Type: Double Required: Yes CustomizedMetricSpeciﬁcation A customized metric. You can specify either a predeﬁned metric or a customized metric. Type: CustomizedMetricSpeciﬁcation object Required: No DisableScaleIn Indicates whether scale in by the target tracking scaling policy is disabled. If the value is true, scale in is disabled and the target tracking scaling policy won't remove capacity from the scalable target. Otherwise, scale in is enabled and the target tracking scaling policy can remove capacity from the scalable target. The default value is false. TargetTrackingScalingPolicyConﬁguration
app-as-api-049	app-as-api.pdf	49	metric, specify the target utilization as the optimal average request count per target during any one-minute interval. Type: Double Required: Yes CustomizedMetricSpeciﬁcation A customized metric. You can specify either a predeﬁned metric or a customized metric. Type: CustomizedMetricSpeciﬁcation object Required: No DisableScaleIn Indicates whether scale in by the target tracking scaling policy is disabled. If the value is true, scale in is disabled and the target tracking scaling policy won't remove capacity from the scalable target. Otherwise, scale in is enabled and the target tracking scaling policy can remove capacity from the scalable target. The default value is false. TargetTrackingScalingPolicyConﬁguration API Version 2016-02-06 201 Application Auto Scaling Type: Boolean Required: No PredeﬁnedMetricSpeciﬁcation API Reference A predeﬁned metric. You can specify either a predeﬁned metric or a customized metric. Type: PredeﬁnedMetricSpeciﬁcation object Required: No ScaleInCooldown The amount of time, in seconds, after a scale-in activity completes before another scale-in activity can start. For more information and for default values, see Deﬁne cooldown periods in the Application Auto Scaling User Guide. Type: Integer Required: No ScaleOutCooldown The amount of time, in seconds, to wait for a previous scale-out activity to take eﬀect. For more information and for default values, see Deﬁne cooldown periods in the Application Auto Scaling User Guide. Type: Integer Required: No See Also For more information about using this API in one of the language-speciﬁc AWS SDKs, see the following: • AWS SDK for C++ • AWS SDK for Java V2 • AWS SDK for Ruby V3 See Also API Version 2016-02-06 202 Application Auto Scaling API Reference Common Parameters The following list contains the parameters that all actions use for signing Signature Version 4 requests with a query string. Any action-speciﬁc parameters are listed in the topic for that action. For more information about Signature Version 4, see Signing AWS API requests in the IAM User Guide. Action The action to be performed. Type: string Required: Yes Version The API version that the request is written for, expressed in the format YYYY-MM-DD. Type: string Required: Yes X-Amz-Algorithm The hash algorithm that you used to create the request signature. Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header. Type: string Valid Values: AWS4-HMAC-SHA256 Required: Conditional X-Amz-Credential The credential scope value, which is a string that includes your access key, the date, the region you are targeting, the service you are requesting, and a termination string ("aws4_request"). The value is expressed in the following format: access_key/YYYYMMDD/region/service/ aws4_request. API Version 2016-02-06 203 Application Auto Scaling API Reference For more information, see Create a signed AWS API request in the IAM User Guide. Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header. Type: string Required: Conditional X-Amz-Date The date that is used to create the signature. The format must be ISO 8601 basic format (YYYYMMDD'T'HHMMSS'Z'). For example, the following date time is a valid X-Amz-Date value: 20120325T120000Z. Condition: X-Amz-Date is optional for all requests; it can be used to override the date used for signing requests. If the Date header is speciﬁed in the ISO 8601 basic format, X-Amz-Date is not required. When X-Amz-Date is used, it always overrides the value of the Date header. For more information, see Elements of an AWS API request signature in the IAM User Guide. Type: string Required: Conditional X-Amz-Security-Token The temporary security token that was obtained through a call to AWS Security Token Service (AWS STS). For a list of services that support temporary security credentials from AWS STS, see AWS services that work with IAM in the IAM User Guide. Condition: If you're using temporary security credentials from AWS STS, you must include the security token. Type: string Required: Conditional X-Amz-Signature Speciﬁes the hex-encoded signature that was calculated from the string to sign and the derived signing key. Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header. API Version 2016-02-06 204 Application Auto Scaling Type: string Required: Conditional X-Amz-SignedHeaders API Reference Speciﬁes all the HTTP headers that were included as part of the canonical request. For more information about specifying signed headers, see Create a signed AWS API request in the IAM User Guide. Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header. Type: string Required: Conditional API Version 2016-02-06 205 Application Auto Scaling API Reference Common Errors This section lists the errors common to the API actions of all AWS services. For errors speciﬁc to an API action for this service, see the topic for that API action. AccessDeniedException You do not have suﬃcient access to perform this action. HTTP Status Code: 400 IncompleteSignature The request signature does not
app-as-api-050	app-as-api.pdf	50	headers, see Create a signed AWS API request in the IAM User Guide. Condition: Specify this parameter when you include authentication information in a query string instead of in the HTTP authorization header. Type: string Required: Conditional API Version 2016-02-06 205 Application Auto Scaling API Reference Common Errors This section lists the errors common to the API actions of all AWS services. For errors speciﬁc to an API action for this service, see the topic for that API action. AccessDeniedException You do not have suﬃcient access to perform this action. HTTP Status Code: 400 IncompleteSignature The request signature does not conform to AWS standards. HTTP Status Code: 400 InternalFailure The request processing has failed because of an unknown error, exception or failure. HTTP Status Code: 500 InvalidAction The action or operation requested is invalid. Verify that the action is typed correctly. HTTP Status Code: 400 InvalidClientTokenId The X.509 certiﬁcate or AWS access key ID provided does not exist in our records. HTTP Status Code: 403 NotAuthorized You do not have permission to perform this action. HTTP Status Code: 400 OptInRequired The AWS access key ID needs a subscription for the service. HTTP Status Code: 403 API Version 2016-02-06 206 Application Auto Scaling RequestExpired API Reference The request reached the service more than 15 minutes after the date stamp on the request or more than 15 minutes after the request expiration date (such as for pre-signed URLs), or the date stamp on the request is more than 15 minutes in the future. HTTP Status Code: 400 ServiceUnavailable The request has failed due to a temporary failure of the server. HTTP Status Code: 503 ThrottlingException The request was denied due to request throttling. HTTP Status Code: 400 ValidationError The input fails to satisfy the constraints speciﬁed by an AWS service. HTTP Status Code: 400 API Version 2016-02-06 207
app-as-ug-001	app-as-ug.pdf	1	User Guide Application Auto Scaling Copyright © 2025 Amazon Web Services, Inc. and/or its aﬃliates. All rights reserved. Application Auto Scaling User Guide Application Auto Scaling: User Guide Copyright © 2025 Amazon Web Services, Inc. and/or its aﬃliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be aﬃliated with, connected to, or sponsored by Amazon. Application Auto Scaling Table of Contents User Guide What is Application Auto Scaling? ................................................................................................. 1 Features of Application Auto Scaling ....................................................................................................... 2 Work with Application Auto Scaling ........................................................................................................ 2 Concepts ......................................................................................................................................................... 3 Learn more ............................................................................................................................................... 5 Services that integrate .................................................................................................................... 6 Amazon AppStream 2.0 .............................................................................................................................. 8 Service-linked role .................................................................................................................................. 8 Service principal ...................................................................................................................................... 9 Registering AppStream 2.0 ﬂeets as scalable targets with Application Auto Scaling ............... 9 Related resources .................................................................................................................................. 10 Amazon Aurora ........................................................................................................................................... 10 Service-linked role ................................................................................................................................ 10 Service principal .................................................................................................................................... 10 Registering Aurora DB clusters as scalable targets with Application Auto Scaling .................. 11 Related resources .................................................................................................................................. 12 Amazon Comprehend ................................................................................................................................ 12 Service-linked role ................................................................................................................................ 12 Service principal .................................................................................................................................... 12 Registering Amazon Comprehend resources as scalable targets with Application Auto Scaling ..................................................................................................................................................... 12 Related resources .................................................................................................................................. 14 Amazon DynamoDB ................................................................................................................................... 14 Service-linked role ................................................................................................................................ 14 Service principal .................................................................................................................................... 14 Registering DynamoDB resources as scalable targets with Application Auto Scaling ............. 15 Related resources .................................................................................................................................. 17 Amazon ECS ................................................................................................................................................ 17 Service-linked role ................................................................................................................................ 18 Service principal .................................................................................................................................... 18 Registering ECS services as scalable targets with Application Auto Scaling ............................. 18 Related resources .................................................................................................................................. 19 Amazon ElastiCache ................................................................................................................................... 20 Service-linked role ................................................................................................................................ 20 iii Application Auto Scaling User Guide Service principal .................................................................................................................................... 20 Registering ElastiCache resources as scalable targets with Application Auto Scaling ............. 20 Related resources .................................................................................................................................. 22 Amazon Keyspaces (for Apache Cassandra) ......................................................................................... 22 Service-linked role ................................................................................................................................ 22 Service principal .................................................................................................................................... 23 Registering Amazon Keyspaces tables as scalable targets with Application Auto Scaling ...... 23 Related resources .................................................................................................................................. 24 AWS Lambda ............................................................................................................................................... 24 Service-linked role ................................................................................................................................ 25 Service principal .................................................................................................................................... 25 Registering Lambda functions as scalable targets with Application Auto Scaling ................... 25 Related resources .................................................................................................................................. 26 Amazon Managed Streaming for Apache Kafka (MSK) ...................................................................... 26 Service-linked role ................................................................................................................................ 26 Service principal .................................................................................................................................... 27 Registering Amazon MSK cluster storage as scalable targets with Application Auto Scaling ..................................................................................................................................................... 27 Related resources .................................................................................................................................. 28 Amazon Neptune ........................................................................................................................................ 28 Service-linked role ................................................................................................................................ 28 Service principal .................................................................................................................................... 29 Registering Neptune clusters as scalable targets with Application Auto Scaling ..................... 29 Related resources .................................................................................................................................. 30 Amazon SageMaker AI .............................................................................................................................. 30 Service-linked role ................................................................................................................................ 30 Service principal .................................................................................................................................... 30 Registering SageMaker AI endpoint variants as scalable targets with Application Auto Scaling ..................................................................................................................................................... 31 Registering the provisioned concurrency of serverless endpoints as scalable targets with Application Auto Scaling ..................................................................................................................... 32 Registering inference components as scalable targets with Application Auto Scaling ........... 33 Related resources .................................................................................................................................. 33 Spot Fleet (Amazon EC2) ......................................................................................................................... 34 Service-linked role ................................................................................................................................ 34 Service principal .................................................................................................................................... 34 iv Application Auto Scaling User Guide Registering Spot Fleets as scalable targets with Application Auto Scaling ............................... 35 Related resources .................................................................................................................................. 36 Amazon WorkSpaces ................................................................................................................................. 36 Service-linked role ................................................................................................................................ 36 Service principal .................................................................................................................................... 36 Registering WorkSpaces pools as scalable targets with Application Auto Scaling .................. 36 Related resources .................................................................................................................................. 37 Custom resources ....................................................................................................................................... 37 Service-linked role ................................................................................................................................ 38 Service principal .................................................................................................................................... 38 Registering custom resources as scalable targets with Application Auto Scaling .................... 38 Related resources .................................................................................................................................. 39 Conﬁgure scaling using AWS CloudFormation ............................................................................ 40 Application Auto Scaling and AWS CloudFormation templates ....................................................... 40 Example template snippets ..................................................................................................................... 41 Learn more about AWS CloudFormation .............................................................................................. 41 Scheduled scaling .......................................................................................................................... 42 How scheduled scaling works .................................................................................................................. 43 How it works .......................................................................................................................................... 43 Considerations ....................................................................................................................................... 43 Commonly used commands ............................................................................................................... 44 Related resources .................................................................................................................................. 45 Limitations .............................................................................................................................................. 45 Create scheduled actions .......................................................................................................................... 45 Create a scheduled action that occurs only once .......................................................................... 46 Create a scheduled action that runs on a recurring interval ....................................................... 47 Create a scheduled action that runs on a recurring schedule ..................................................... 48 Create a one-time scheduled action that speciﬁes a time zone ................................................. 49 Create a recurring scheduled action that speciﬁes a time zone .................................................. 50 Describe scheduled scaling ...................................................................................................................... 50 Describe scaling activities for a service ............................................................................................ 51 Describe the scheduled actions for a service .................................................................................. 53 Describe the scheduled actions for a scalable target .................................................................... 55 Schedule recurring scaling actions ......................................................................................................... 56 Turn oﬀ scheduled scaling ....................................................................................................................... 59 Delete a scheduled action ........................................................................................................................ 60 v Application Auto Scaling User Guide Target tracking scaling policies .................................................................................................... 62 How target tracking works ...................................................................................................................... 63 How it works .......................................................................................................................................... 63 Choose metrics ...................................................................................................................................... 64
app-as-ug-002	app-as-ug.pdf	2	a one-time scheduled action that speciﬁes a time zone ................................................. 49 Create a recurring scheduled action that speciﬁes a time zone .................................................. 50 Describe scheduled scaling ...................................................................................................................... 50 Describe scaling activities for a service ............................................................................................ 51 Describe the scheduled actions for a service .................................................................................. 53 Describe the scheduled actions for a scalable target .................................................................... 55 Schedule recurring scaling actions ......................................................................................................... 56 Turn oﬀ scheduled scaling ....................................................................................................................... 59 Delete a scheduled action ........................................................................................................................ 60 v Application Auto Scaling User Guide Target tracking scaling policies .................................................................................................... 62 How target tracking works ...................................................................................................................... 63 How it works .......................................................................................................................................... 63 Choose metrics ...................................................................................................................................... 64 Deﬁne target value ............................................................................................................................... 65 Deﬁne cooldown periods .................................................................................................................... 66 Considerations ....................................................................................................................................... 67 Multiple scaling policies ...................................................................................................................... 68 Commonly used commands ............................................................................................................... 69 Related resources .................................................................................................................................. 69 Limitations .............................................................................................................................................. 69 Create a target tracking scaling policy .................................................................................................. 70 Step 1: Register a scalable target ..................................................................................................... 70 Step 2: Create a target tracking scaling policy .............................................................................. 71 Step 3: Describe target tracking scaling policies ........................................................................... 73 Delete a target tracking scaling policy .................................................................................................. 75 Use metric math ......................................................................................................................................... 75 Example: Amazon SQS queue backlog per task ............................................................................. 76 Limitations .............................................................................................................................................. 80 Step scaling policies ...................................................................................................................... 81 How step scaling works ............................................................................................................................ 82 How it works .......................................................................................................................................... 82 Step adjustments .................................................................................................................................. 83 Scaling adjustment types .................................................................................................................... 85 Cooldown period ................................................................................................................................... 86 Commonly used commands ............................................................................................................... 87 Considerations ....................................................................................................................................... 87 Related resources .................................................................................................................................. 45 Console access ....................................................................................................................................... 88 Create a step scaling policy ..................................................................................................................... 88 Step 1: Register a scalable target .................................................................................................... 88 Step 2: Create a step scaling policy ................................................................................................. 89 Step 3: Create an alarm that invokes a scaling policy .................................................................. 93 Describe step scaling policies .................................................................................................................. 94 Delete a step scaling policy ..................................................................................................................... 96 Predictive scaling ........................................................................................................................... 97 vi Application Auto Scaling User Guide How it works ............................................................................................................................................... 97 Maximum capacity limit ...................................................................................................................... 98 Commonly used commands for scaling policy creation, management, and deletion ............. 99 Considerations ....................................................................................................................................... 99 Create a predictive scaling policy ......................................................................................................... 100 Override the forecast .............................................................................................................................. 101 Step 1: (Optional) Analyze time series data ................................................................................. 102 Step 2: Create two scheduled actions ........................................................................................... 102 Use custom metrics ................................................................................................................................. 104 Best practices ...................................................................................................................................... 104 Prerequisites ........................................................................................................................................ 105 Constructing the JSON for custom metrics .................................................................................. 105 Considerations for custom metrics ................................................................................................. 114 Tutorial: Conﬁgure auto scaling to handle a heavy workload .................................................. 115 Prerequisites .............................................................................................................................................. 115 Step 1: Register your scalable target .................................................................................................. 116 Step 2: Set up scheduled actions according to your requirements ............................................... 117 Step 3: Add a target tracking scaling policy ...................................................................................... 120 Step 4: Next steps ................................................................................................................................... 123 Step 5: Clean up ...................................................................................................................................... 123 Suspend scaling ........................................................................................................................... 126 Scaling activities ....................................................................................................................................... 126 Suspend and resume scaling activities ................................................................................................ 127 View suspended scaling activities ................................................................................................... 130 Resume scaling activities .................................................................................................................. 130 Scaling activities .......................................................................................................................... 132 Look up scaling activities by scalable target ..................................................................................... 132 Include not scaled activities .................................................................................................................. 133 Reason codes ............................................................................................................................................ 135 Monitoring ................................................................................................................................... 138 Monitor using CloudWatch .................................................................................................................... 139 CloudWatch metrics for monitoring resource usage ................................................................... 140 Predeﬁned metrics for target tracking scaling policies .............................................................. 153 Log API calls using CloudTrail ............................................................................................................... 156 Application Auto Scaling management events in CloudTrail .................................................... 157 Application Auto Scaling event examples ..................................................................................... 158 vii Application Auto Scaling User Guide Application Auto Scaling RemoveAction calls on CloudWatch ................................................. 159 Amazon EventBridge ............................................................................................................................... 159 Application Auto Scaling events ..................................................................................................... 159 Working with AWS SDKs ............................................................................................................. 164 Code examples ............................................................................................................................. 166 Basics .......................................................................................................................................................... 166 Actions .................................................................................................................................................. 167 Tagging support .......................................................................................................................... 200 Tagging example ...................................................................................................................................... 200 Tags for security ....................................................................................................................................... 201 Control access to tags ............................................................................................................................ 202 Security ........................................................................................................................................ 204 Data protection ........................................................................................................................................ 204 Identity and Access Management ........................................................................................................ 205 Access control ...................................................................................................................................... 206 How Application Auto Scaling works with IAM ............................................................................ 206 AWS managed policies ...................................................................................................................... 212 Service-linked roles ............................................................................................................................ 222 Identity-based policy examples ....................................................................................................... 228 Troubleshooting .................................................................................................................................. 240 Permissions validation ....................................................................................................................... 241 AWS PrivateLink ....................................................................................................................................... 243 Create an interface VPC endpoint .................................................................................................. 244 Create a VPC endpoint policy .......................................................................................................... 244 Resilience ................................................................................................................................................... 245 Infrastructure security ............................................................................................................................. 245 Compliance validation ............................................................................................................................ 246 Quotas .......................................................................................................................................... 248 Document history ........................................................................................................................ 249 viii Application Auto Scaling User Guide What is Application Auto Scaling? Application Auto Scaling is a web service for developers and system administrators who need a solution for automatically scaling their scalable resources for individual AWS services beyond Amazon EC2 Auto Scaling. With Application Auto Scaling, you can conﬁgure automatic scaling for the following resources: • AppStream 2.0 ﬂeets • Aurora replicas • Amazon Comprehend document classiﬁcation and entity recognizer endpoints • DynamoDB tables and global secondary indexes • Amazon ECS services • ElastiCache replication groups (Redis OSS and Valkey) and Memcached clusters • Amazon EMR clusters • Amazon Keyspaces (for Apache Cassandra) tables • Lambda function provisioned concurrency • Amazon Managed Streaming for Apache Kafka (MSK) broker storage • Amazon Neptune clusters • SageMaker AI endpoint variants • SageMaker
app-as-ug-003	app-as-ug.pdf	3	their scalable resources for individual AWS services beyond Amazon EC2 Auto Scaling. With Application Auto Scaling, you can conﬁgure automatic scaling for the following resources: • AppStream 2.0 ﬂeets • Aurora replicas • Amazon Comprehend document classiﬁcation and entity recognizer endpoints • DynamoDB tables and global secondary indexes • Amazon ECS services • ElastiCache replication groups (Redis OSS and Valkey) and Memcached clusters • Amazon EMR clusters • Amazon Keyspaces (for Apache Cassandra) tables • Lambda function provisioned concurrency • Amazon Managed Streaming for Apache Kafka (MSK) broker storage • Amazon Neptune clusters • SageMaker AI endpoint variants • SageMaker AI inference components • SageMaker AI Serverless provisioned concurrency • Spot Fleet requests • Pool of Amazon WorkSpaces • Custom resources provided by your own applications or services. For more information, see the GitHub repository. To see the regional availability for any of the AWS services listed above, see the Region table. For information about scaling your ﬂeet of Amazon EC2 instances using Auto Scaling groups, see the Amazon EC2 Auto Scaling User Guide. 1 Application Auto Scaling User Guide Features of Application Auto Scaling Application Auto Scaling allows you to automatically scale your scalable resources according to conditions that you deﬁne. • Target tracking scaling – Scale a resource based on a target value for a speciﬁc CloudWatch metric. • Step scaling – Scale a resource based on a set of scaling adjustments that vary based on the size of the alarm breach. • Scheduled scaling – Scale a resource one time only or on a recurring schedule. • Predictive scaling – Scale a resource proactively to match anticipated load based on historical data. Work with Application Auto Scaling You can conﬁgure scaling using the following interfaces depending on the resource that you are scaling: • AWS Management Console – Provides a web interface that you can use to conﬁgure scaling. Sign up for an AWS account and sign into the AWS Management Console. Then, open the service console for one of the resources listed in the introduction. For example, to scale a Lambda function, open the AWS Lambda console. Ensure that you open the console in the same AWS Region as the resource that you want to work with. Note Console access is not available for all resources. For more information, see AWS services that you can use with Application Auto Scaling. • AWS Command Line Interface (AWS CLI) – Provides commands for a broad set of AWS services, and is supported on Windows, macOS, and Linux. To get started, see AWS Command Line Interface. For a list of commands, see application-autoscaling in the AWS CLI Command Reference. • AWS Tools for Windows PowerShell – Provides commands for a broad set of AWS products for those who script in the PowerShell environment. To get started, see the AWS Tools for Features of Application Auto Scaling 2 Application Auto Scaling User Guide Windows PowerShell User Guide. For more information, see the AWS Tools for PowerShell Cmdlet Reference. • AWS SDKs – Provides language-speciﬁc API operations and takes care of many of the connection details, such as calculating signatures, handling request retries, and handling errors. For more information, see Tools to Build on AWS. • HTTPS API – Provides low-level API actions that you call using HTTPS requests. For more information, see the Application Auto Scaling API Reference. • AWS CloudFormation – Supports conﬁguring scaling using a CloudFormation template. For more information, see Conﬁgure Application Auto Scaling resources using AWS CloudFormation. To connect programmatically to an AWS service, you use an endpoint. For information about endpoints for calls to Application Auto Scaling, see Application Auto Scaling endpoints and quotas in the AWS General Reference. Application Auto Scaling concepts This topic explains key concepts to help you learn about Application Auto Scaling and start using it. Scalable target An entity that you create to specify the resource that you want to scale. Each scalable target is uniquely identiﬁed by a service namespace, resource ID, and scalable dimension, which represents some capacity dimension of the underlying service. For example, an Amazon ECS service supports auto scaling of its task count, a DynamoDB table supports auto scaling of the read and write capacity of the table and its global secondary indexes, and an Aurora cluster supports scaling of its replica count. Tip Each scalable target also has a minimum and maximum capacity. Scaling policies will never go higher or lower than the minimum-maximum range. You can make out-of- band changes directly to the underlying resource that are outside of this range, which Application Auto Scaling doesn't know about. However, anytime a scaling policy is invoked or the RegisterScalableTarget API is called, Application Auto Scaling retrieves the current capacity and compares it to the minimum and maximum capacity. Concepts 3 Application Auto Scaling User
app-as-ug-004	app-as-ug.pdf	4	of the table and its global secondary indexes, and an Aurora cluster supports scaling of its replica count. Tip Each scalable target also has a minimum and maximum capacity. Scaling policies will never go higher or lower than the minimum-maximum range. You can make out-of- band changes directly to the underlying resource that are outside of this range, which Application Auto Scaling doesn't know about. However, anytime a scaling policy is invoked or the RegisterScalableTarget API is called, Application Auto Scaling retrieves the current capacity and compares it to the minimum and maximum capacity. Concepts 3 Application Auto Scaling User Guide If it falls outside of the minimum-maximum range, then the capacity is updated to comply with the set minimum and maximum. Scale in When Application Auto Scaling automatically decreases capacity for a scalable target, the scalable target scales in. When scaling policies are set, they cannot scale in the scalable target lower than its minimum capacity. Scale out When Application Auto Scaling automatically increases capacity for a scalable target, the scalable target scales out. When scaling policies are set, they cannot scale out the scalable target higher than its maximum capacity. Scaling policy A scaling policy instructs Application Auto Scaling to track a speciﬁc CloudWatch metric. Then, it determines what scaling action to take when the metric is higher or lower than a certain threshold value. For example, you might want to scale out if the CPU usage across your cluster starts to rise, and scale in when it drops again. The metrics that are used for auto scaling are published by the target service, but you can also publish your own metric to CloudWatch and then use it with a scaling policy. A cooldown period between scaling activities lets the resource stabilize before another scaling activity starts. Application Auto Scaling continues to evaluate metrics during the cooldown period. When the cooldown period ends, the scaling policy initiates another scaling activity if needed. While a cooldown period is in eﬀect, if a larger scale out is necessary based on the current metric value, the scaling policy scales out immediately. Scheduled action Scheduled actions automatically scale resources at a speciﬁc date and time. They work by modifying the minimum and maximum capacity for a scalable target, and therefore can be used to scale in and out on a schedule by setting the minimum capacity high or the maximum capacity low. For example, you can use scheduled actions to scale an application that doesn't consume resources on weekends by decreasing capacity on Friday and increasing capacity on the following Monday. Concepts 4 Application Auto Scaling User Guide You can also use scheduled actions to optimize the minimum and maximum values over time to adapt to situations where higher than normal traﬃc is expected, for example, marketing campaigns or seasonal ﬂuctuations. Doing this can help you improve performance for times when you need to scale out higher for the increasing usage, and reduce costs at times when you use fewer resources. Learn more AWS services that you can use with Application Auto Scaling — This section introduces you to the services that you can scale and helps you set up auto scaling by registering a scalable target. It also describes each of the IAM service-linked roles that Application Auto Scaling creates to access resources in the target service. Target tracking scaling policies for Application Auto Scaling — One of the primary features of Application Auto Scaling is target tracking scaling policies. Learn how target tracking policies automatically adjust desired capacity to keep utilization at a constant level based on your conﬁgured metric and target values. For example, you can conﬁgure target tracking to keep the average CPU utilization for your Spot Fleet at 50 percent. Application Auto Scaling then launches or terminates EC2 instances as required to keep the aggregated CPU utilization across all servers at 50 percent. Learn more 5 Application Auto Scaling User Guide AWS services that you can use with Application Auto Scaling Application Auto Scaling integrates with other AWS services so that you can add scaling capabilities to meet your application's demand. Auto scaling is an optional feature of the service that is disabled by default in almost all cases. The following table lists the AWS services that you can use with Application Auto Scaling, including information about supported methods for conﬁguring auto scaling. You can also use Application Auto Scaling with custom resources. • Console access – You can conﬁgure a compatible AWS service to start auto scaling by conﬁguring a scaling policy in the console of the target service. • CLI access – You can conﬁgure a compatible AWS service to start auto scaling using the AWS CLI. • SDK access – You can conﬁgure a compatible AWS service to start auto scaling using the AWS
app-as-ug-005	app-as-ug.pdf	5	all cases. The following table lists the AWS services that you can use with Application Auto Scaling, including information about supported methods for conﬁguring auto scaling. You can also use Application Auto Scaling with custom resources. • Console access – You can conﬁgure a compatible AWS service to start auto scaling by conﬁguring a scaling policy in the console of the target service. • CLI access – You can conﬁgure a compatible AWS service to start auto scaling using the AWS CLI. • SDK access – You can conﬁgure a compatible AWS service to start auto scaling using the AWS SDKs. • CloudFormation access – You can conﬁgure a compatible AWS service to start auto scaling using an AWS CloudFormation stack template. For more information, see Conﬁgure Application Auto Scaling resources using AWS CloudFormation. AWS service Console access¹ CLI access SDK access CloudFormation access AppStream 2.0 Aurora Amazon Comprehend Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes 6 Application Auto Scaling User Guide AWS service Console access¹ CLI access SDK access CloudFormation access Amazon DynamoDB Amazon ECS Amazon ElastiCache Amazon EMR Amazon Keyspaces Lambda Amazon MSK Amazon Neptune Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 7 Application Auto Scaling User Guide AWS service Console access¹ SageMaker AI CLI access SDK access CloudFormation access Spot Fleet WorkSpaces Custom resources Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes ¹ Console access for conﬁguring scaling policies. Most services don't support conﬁguring scheduled scaling from the console. Currently, only Amazon AppStream 2.0, ElastiCache, and Spot Fleet provide console access for scheduled scaling. Amazon AppStream 2.0 and Application Auto Scaling You can scale AppStream 2.0 ﬂeets using target tracking scaling policies, step scaling policies, and scheduled scaling. Use the following information to help you integrate AppStream 2.0 with Application Auto Scaling. Service-linked role created for AppStream 2.0 The following service-linked role is automatically created in your AWS account when registering AppStream 2.0 resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. Amazon AppStream 2.0 8 Application Auto Scaling User Guide • AWSServiceRoleForApplicationAutoScaling_AppStreamFleet Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: • appstream.application-autoscaling.amazonaws.com Registering AppStream 2.0 ﬂeets as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for an AppStream 2.0 ﬂeet. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. If you conﬁgure auto scaling using the AppStream 2.0 console, then AppStream 2.0 automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for an AppStream 2.0 ﬂeet. The following example registers the desired capacity of a ﬂeet called sample-fleet, with a minimum capacity of one ﬂeet instance and a maximum capacity of ﬁve ﬂeet instances. aws application-autoscaling register-scalable-target \ --service-namespace appstream \ --scalable-dimension appstream:fleet:DesiredCapacity \ --resource-id fleet/sample-fleet \ --min-capacity 1 \ --max-capacity 5 If successful, this command returns the ARN of the scalable target. Service principal 9 Application Auto Scaling User Guide { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources For more information, see Fleet Auto Scaling for Amazon AppStream 2.0 in the Amazon AppStream 2.0 Administration Guide. Amazon Aurora and Application Auto Scaling You can scale Aurora DB clusters using target tracking scaling policies, step scaling policies, and scheduled scaling. Use the following information to help you integrate Aurora with Application Auto Scaling. Service-linked role created for Aurora The following service-linked role is automatically created in your AWS account when registering Aurora resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_RDSCluster Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the
app-as-ug-006	app-as-ug.pdf	6	Aurora with Application Auto Scaling. Service-linked role created for Aurora The following service-linked role is automatically created in your AWS account when registering Aurora resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_RDSCluster Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: • rds.application-autoscaling.amazonaws.com Related resources 10 Application Auto Scaling User Guide Registering Aurora DB clusters as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for an Aurora cluster. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. If you conﬁgure auto scaling using the Aurora console, then Aurora automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for an Aurora cluster. The following example registers the count of Aurora Replicas in a cluster called my-db-cluster, with a minimum capacity of one Aurora Replica and a maximum capacity of eight Aurora Replicas. aws application-autoscaling register-scalable-target \ --service-namespace rds \ --scalable-dimension rds:cluster:ReadReplicaCount \ --resource-id cluster:my-db-cluster \ --min-capacity 1 \ --max-capacity 8 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Registering Aurora DB clusters as scalable targets with Application Auto Scaling 11 Application Auto Scaling Related resources User Guide For more information, see Amazon Aurora Auto Scaling with Aurora Replicas in the Amazon RDS User Guide for Aurora. Amazon Comprehend and Application Auto Scaling You can scale Amazon Comprehend document classiﬁcation and entity recognizer endpoints using target tracking scaling policies and scheduled scaling. Use the following information to help you integrate Amazon Comprehend with Application Auto Scaling. Service-linked role created for Amazon Comprehend The following service-linked role is automatically created in your AWS account when registering Amazon Comprehend resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_ComprehendEndpoint Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: • comprehend.application-autoscaling.amazonaws.com Registering Amazon Comprehend resources as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for an Amazon Comprehend document classiﬁcation or entity recognizer endpoint. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Related resources 12 Application Auto Scaling User Guide Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. To conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for a document classiﬁcation endpoint. The following example registers the desired number of inference units to be used by the model for a document classiﬁer endpoint using the endpoint's ARN, with a minimum capacity of one inference unit and a maximum capacity of three inference units. aws application-autoscaling register-scalable-target \ --service-namespace comprehend \ --scalable-dimension comprehend:document-classifier-endpoint:DesiredInferenceUnits \ --resource-id arn:aws:comprehend:us-west-2:123456789012:document-classifier- endpoint/EXAMPLE \ --min-capacity 1 \ --max-capacity 3 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Call the register-scalable-target command for an entity recognizer endpoint. The following example registers the desired number of inference units to be used by the model for an entity recognizer using the endpoint's ARN, with a minimum capacity of one inference unit and a maximum capacity of three inference units. aws application-autoscaling register-scalable-target \ --service-namespace comprehend \ --scalable-dimension comprehend:entity-recognizer-endpoint:DesiredInferenceUnits \ --resource-id arn:aws:comprehend:us-west-2:123456789012:entity-recognizer- endpoint/EXAMPLE \ --min-capacity 1 \ Registering Amazon Comprehend resources as scalable targets with Application Auto Scaling 13 Application Auto Scaling --max-capacity 3 User Guide If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources For more information, see Auto scaling with endpoints in
app-as-ug-007	app-as-ug.pdf	7	using the endpoint's ARN, with a minimum capacity of one inference unit and a maximum capacity of three inference units. aws application-autoscaling register-scalable-target \ --service-namespace comprehend \ --scalable-dimension comprehend:entity-recognizer-endpoint:DesiredInferenceUnits \ --resource-id arn:aws:comprehend:us-west-2:123456789012:entity-recognizer- endpoint/EXAMPLE \ --min-capacity 1 \ Registering Amazon Comprehend resources as scalable targets with Application Auto Scaling 13 Application Auto Scaling --max-capacity 3 User Guide If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources For more information, see Auto scaling with endpoints in the Amazon Comprehend Developer Guide. Amazon DynamoDB and Application Auto Scaling You can scale DynamoDB tables and global secondary indexes using target tracking scaling policies and scheduled scaling. Use the following information to help you integrate DynamoDB with Application Auto Scaling. Service-linked role created for DynamoDB The following service-linked role is automatically created in your AWS account when registering DynamoDB resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_DynamoDBTable Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: Related resources 14 Application Auto Scaling User Guide • dynamodb.application-autoscaling.amazonaws.com Registering DynamoDB resources as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for a DynamoDB table or global secondary index. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. If you conﬁgure auto scaling using the DynamoDB console, then DynamoDB automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for a table's write capacity. The following example registers the provisioned write capacity of a table called my-table, with a minimum capacity of ﬁve write capacity units and a maximum capacity of 10 write capacity units: aws application-autoscaling register-scalable-target \ --service-namespace dynamodb \ --scalable-dimension dynamodb:table:WriteCapacityUnits \ --resource-id table/my-table \ --min-capacity 5 \ --max-capacity 10 If successful, this command returns the ARN of the scalable target: { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Call the register-scalable-target command for a table's read capacity. The following example registers the provisioned read capacity of a table called my-table, with a minimum capacity of ﬁve read capacity units and a maximum capacity of 10 read units: Registering DynamoDB resources as scalable targets with Application Auto Scaling 15 Application Auto Scaling User Guide aws application-autoscaling register-scalable-target \ --service-namespace dynamodb \ --scalable-dimension dynamodb:table:ReadCapacityUnits \ --resource-id table/my-table \ --min-capacity 5 \ --max-capacity 10 If successful, this command returns the ARN of the scalable target: { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Call the register-scalable-target command for the write capacity of a global secondary index. The following example registers the provisioned write capacity of a global secondary index called my-table-index, with a minimum capacity of ﬁve write capacity units and a maximum capacity of 10 write capacity units: aws application-autoscaling register-scalable-target \ --service-namespace dynamodb \ --scalable-dimension dynamodb:index:WriteCapacityUnits \ --resource-id table/my-table/index/my-table-index \ --min-capacity 5 \ --max-capacity 10 If successful, this command returns the ARN of the scalable target: { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Call the register-scalable-target command for the read capacity of a global secondary index. The following example registers the provisioned read capacity of a global secondary index called my- table-index, with a minimum capacity of ﬁve read capacity units and a maximum capacity of 10 read capacity units: Registering DynamoDB resources as scalable targets with Application Auto Scaling 16 Application Auto Scaling User Guide aws application-autoscaling register-scalable-target \ --service-namespace dynamodb \ --scalable-dimension dynamodb:index:ReadCapacityUnits \ --resource-id table/my-table/index/my-table-index \ --min-capacity 5 \ --max-capacity 10 If successful, this command returns the ARN of the scalable target: { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources If you are just getting started with Application Auto Scaling, you can ﬁnd additional useful information about scaling your DynamoDB resources in the following documentation: • Managing throughput capacity with DynamoDB Auto Scaling in the Amazon DynamoDB Developer Guide • Evaluate your table's auto scaling settings in the Amazon DynamoDB Developer Guide • How to use AWS CloudFormation to conﬁgure auto scaling for DynamoDB tables and indexes on the AWS Blog Amazon ECS and Application Auto Scaling You can scale ECS services using
app-as-ug-008	app-as-ug.pdf	8	SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources If you are just getting started with Application Auto Scaling, you can ﬁnd additional useful information about scaling your DynamoDB resources in the following documentation: • Managing throughput capacity with DynamoDB Auto Scaling in the Amazon DynamoDB Developer Guide • Evaluate your table's auto scaling settings in the Amazon DynamoDB Developer Guide • How to use AWS CloudFormation to conﬁgure auto scaling for DynamoDB tables and indexes on the AWS Blog Amazon ECS and Application Auto Scaling You can scale ECS services using target tracking scaling policies, predictive scaling policies, step scaling policies, and scheduled scaling. Use the following information to help you integrate Amazon ECS with Application Auto Scaling. Related resources 17 Application Auto Scaling User Guide Service-linked role created for Amazon ECS The following service-linked role is automatically created in your AWS account when registering Amazon ECS resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_ECSService Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: • ecs.application-autoscaling.amazonaws.com Registering ECS services as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for an Amazon ECS service. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. If you conﬁgure auto scaling using the Amazon ECS console, then Amazon ECS automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for an Amazon ECS service. The following example registers a scalable target for a service called sample-app-service, running on the default cluster, with a minimum task count of one task and a maximum task count of 10 tasks. aws application-autoscaling register-scalable-target \ Service-linked role 18 Application Auto Scaling User Guide --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id service/default/sample-app-service \ --min-capacity 1 \ --max-capacity 10 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources If you are just getting started with Application Auto Scaling, you can ﬁnd additional useful information about scaling your Amazon ECS resources in the following documentation: • Service auto scaling in the Amazon Elastic Container Service Developer Guide • Optimize Amazon ECS service auto scaling in the Amazon Elastic Container Service Developer Guide Note For instructions for suspending scale-out processes while Amazon ECS deployments are in progress, see the following documentation: Service auto scaling and deployments in the Amazon Elastic Container Service Developer Guide Related resources 19 Application Auto Scaling User Guide ElastiCache and Application Auto Scaling You can horizontally scale Amazon ElastiCache replication groups (Redis OSS and Valkey) and Memcached self-designed clusters using target tracking scaling policies and scheduled scaling. To integrate ElastiCache with Application Auto Scaling, use the following information. Service-linked role created for ElastiCache The following service-linked role is automatically created in your AWS account when registering ElastiCache resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_ElastiCacheRG Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: • elasticache.application-autoscaling.amazonaws.com Registering ElastiCache resources as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for an ElastiCache replication group, cluster, or node. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. If you conﬁgure auto scaling using the ElastiCache console, then ElastiCache automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Amazon ElastiCache 20 Application Auto Scaling User Guide Call the register-scalable-target
app-as-ug-009	app-as-ug.pdf	9	create scaling policies or scheduled actions for an ElastiCache replication group, cluster, or node. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. If you conﬁgure auto scaling using the ElastiCache console, then ElastiCache automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Amazon ElastiCache 20 Application Auto Scaling User Guide Call the register-scalable-target command for an ElastiCache replication group. The following example registers the desired number of node groups for a replication group called mycluster1, with a minimum capacity of one and a maximum capacity of ﬁve. aws application-autoscaling register-scalable-target \ --service-namespace elasticache \ --scalable-dimension elasticache:replication-group:NodeGroups \ --resource-id replication-group/mycluster1 \ --min-capacity 1 \ --max-capacity 5 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } The following example registers the desired number of replicas per node group for a replication group called mycluster2, with a minimum capacity of one and a maximum capacity of ﬁve. aws application-autoscaling register-scalable-target \ --service-namespace elasticache \ --scalable-dimension elasticache:replication-group:Replicas \ --resource-id replication-group/mycluster2 \ --min-capacity 1 \ --max-capacity 5 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/234abcd56ab78cd901ef1234567890ab1234" } The following example registers the desired number of nodes for a cluster called mynode1, with a minimum capacity of 20 and a maximum capacity of 50. aws application-autoscaling register-scalable-target \ Registering ElastiCache resources as scalable targets with Application Auto Scaling 21 Application Auto Scaling User Guide --service-namespace elasticache \ --scalable-dimension elasticache:cache-cluster:Nodes \ --resource-id cache-cluster/mynode1 \ --min-capacity 20 \ --max-capacity 50 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/01234abcd56ab78cd901ef1234567890ab12" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources For more information, see Auto Scaling Valkey and Redis OSS clusters and Scaling clusters for Memcached in the Amazon ElastiCache User Guide. Amazon Keyspaces (for Apache Cassandra) and Application Auto Scaling You can scale Amazon Keyspaces tables using target tracking scaling policies and scheduled scaling. Use the following information to help you integrate Amazon Keyspaces with Application Auto Scaling. Service-linked role created for Amazon Keyspaces The following service-linked role is automatically created in your AWS account when registering Amazon Keyspaces resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. Related resources 22 Application Auto Scaling User Guide • AWSServiceRoleForApplicationAutoScaling_CassandraTable Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: • cassandra.application-autoscaling.amazonaws.com Registering Amazon Keyspaces tables as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for an Amazon Keyspaces table. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. If you conﬁgure auto scaling using the Amazon Keyspaces console, then Amazon Keyspaces automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for an Amazon Keyspaces table. The following example registers the provisioned write capacity of a table called mytable, with a minimum capacity of ﬁve write capacity units and a maximum capacity of 10 write capacity units. aws application-autoscaling register-scalable-target \ --service-namespace cassandra \ --scalable-dimension cassandra:table:WriteCapacityUnits \ --resource-id keyspace/mykeyspace/table/mytable \ --min-capacity 5 \ --max-capacity 10 If successful, this command returns the ARN of the scalable target. Service principal 23 Application Auto Scaling User Guide { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } The following example registers the provisioned read capacity of a table called mytable, with a minimum capacity of ﬁve read capacity units and a maximum capacity of 10 read capacity units. aws application-autoscaling register-scalable-target \ --service-namespace cassandra \ --scalable-dimension cassandra:table:ReadCapacityUnits \ --resource-id keyspace/mykeyspace/table/mytable \ --min-capacity 5 \ --max-capacity 10 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources For more information, see Manage throughput capacity automatically with Amazon Keyspaces auto scaling in the Amazon Keyspaces Developer Guide. AWS Lambda and Application Auto Scaling You can scale AWS
app-as-ug-010	app-as-ug.pdf	10	with a minimum capacity of ﬁve read capacity units and a maximum capacity of 10 read capacity units. aws application-autoscaling register-scalable-target \ --service-namespace cassandra \ --scalable-dimension cassandra:table:ReadCapacityUnits \ --resource-id keyspace/mykeyspace/table/mytable \ --min-capacity 5 \ --max-capacity 10 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources For more information, see Manage throughput capacity automatically with Amazon Keyspaces auto scaling in the Amazon Keyspaces Developer Guide. AWS Lambda and Application Auto Scaling You can scale AWS Lambda provisioned concurrency using target tracking scaling policies and scheduled scaling. Use the following information to help you integrate Lambda with Application Auto Scaling. Related resources 24 Application Auto Scaling User Guide Service-linked role created for Lambda The following service-linked role is automatically created in your AWS account when registering Lambda resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_LambdaConcurrency Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: • lambda.application-autoscaling.amazonaws.com Registering Lambda functions as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for a Lambda function. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. To conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for a Lambda function. The following example registers the provisioned concurrency for an alias called BLUE for a function called my- function, with a minimum capacity of 0 and a maximum capacity of 100. aws application-autoscaling register-scalable-target \ --service-namespace lambda \ --scalable-dimension lambda:function:ProvisionedConcurrency \ --resource-id function:my-function:BLUE \ Service-linked role 25 Application Auto Scaling --min-capacity 0 \ --max-capacity 100 User Guide If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources If you are just getting started with Application Auto Scaling, you can ﬁnd additional useful information about scaling your Lambda functions in the following documentation: • Conﬁguring provisioned concurrency in the AWS Lambda Developer Guide • Scheduling Lambda Provisioned Concurrency for recurring peak usage on the AWS Blog Amazon Managed Streaming for Apache Kafka (MSK) and Application Auto Scaling You can scale out Amazon MSK cluster storage using target tracking scaling policies. Scale in by the target tracking policy is disabled. Use the following information to help you integrate Amazon MSK with Application Auto Scaling. Service-linked role created for Amazon MSK The following service-linked role is automatically created in your AWS account when registering Amazon MSK resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. Related resources 26 Application Auto Scaling User Guide • AWSServiceRoleForApplicationAutoScaling_KafkaCluster Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: • kafka.application-autoscaling.amazonaws.com Registering Amazon MSK cluster storage as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create a scaling policy for the storage volume size per broker of an Amazon MSK cluster. A scalable target is a resource that Application Auto Scaling can scale. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. If you conﬁgure auto scaling using the Amazon MSK console, then Amazon MSK automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for an Amazon MSK cluster. The following example registers the storage volume size per broker of an Amazon MSK cluster, with a minimum capacity of 100 GiB and a maximum capacity of 800 GiB. aws application-autoscaling register-scalable-target \ --service-namespace kafka \ --scalable-dimension kafka:broker-storage:VolumeSize \ --resource-id arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5 \ --min-capacity 100 \ --max-capacity 800 If successful, this command returns the ARN of the scalable target. Service
app-as-ug-011	app-as-ug.pdf	11	a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for an Amazon MSK cluster. The following example registers the storage volume size per broker of an Amazon MSK cluster, with a minimum capacity of 100 GiB and a maximum capacity of 800 GiB. aws application-autoscaling register-scalable-target \ --service-namespace kafka \ --scalable-dimension kafka:broker-storage:VolumeSize \ --resource-id arn:aws:kafka:us-east-1:123456789012:cluster/demo- cluster-1/6357e0b2-0e6a-4b86-a0b4-70df934c2e31-5 \ --min-capacity 100 \ --max-capacity 800 If successful, this command returns the ARN of the scalable target. Service principal 27 Application Auto Scaling User Guide { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Note When an Amazon MSK cluster is the scalable target, scale in is disabled and cannot be enabled. Related resources For more information, see Automatic scaling for Amazon MSK clusters in the Amazon Managed Streaming for Apache Kafka Developer Guide. Amazon Neptune and Application Auto Scaling You can scale Neptune clusters using target tracking scaling policies and scheduled scaling. Use the following information to help you integrate Neptune with Application Auto Scaling. Service-linked role created for Neptune The following service-linked role is automatically created in your AWS account when registering Neptune resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_NeptuneCluster Related resources 28 Application Auto Scaling User Guide Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: • neptune.application-autoscaling.amazonaws.com Registering Neptune clusters as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for a Neptune cluster. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. To conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for a Neptune cluster. The following example registers the desired capacity of a cluster called mycluster, with a minimum capacity of one and a maximum capacity of eight. aws application-autoscaling register-scalable-target \ --service-namespace neptune \ --scalable-dimension neptune:cluster:ReadReplicaCount \ --resource-id cluster:mycluster \ --min-capacity 1 \ --max-capacity 8 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Service principal 29 Application Auto Scaling • AWS SDK: User Guide Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources For more information, see Auto scaling the number of replicas in an Amazon Neptune DB cluster in the Neptune User Guide. Amazon SageMaker AI and Application Auto Scaling You can scale SageMaker AI endpoint variants, provisioned concurrency for serverless endpoints, and inference components using target tracking scaling policies, step scaling policies, and scheduled scaling. Use the following information to help you integrate SageMaker AI with Application Auto Scaling. Service-linked role created for SageMaker AI The following service-linked role is automatically created in your AWS account when registering SageMaker AI resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_SageMakerEndpoint Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: • sagemaker.application-autoscaling.amazonaws.com Related resources 30 Application Auto Scaling User Guide Registering SageMaker AI endpoint variants as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for a SageMaker AI model (variant). A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. If you conﬁgure auto scaling using the SageMaker AI console, then SageMaker AI automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for a product variant. The following example registers the desired instance count for a product variant called my-variant, running on the
app-as-ug-012	app-as-ug.pdf	12	target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. If you conﬁgure auto scaling using the SageMaker AI console, then SageMaker AI automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for a product variant. The following example registers the desired instance count for a product variant called my-variant, running on the my- endpoint endpoint, with a minimum capacity of one instance and a maximum capacity of eight instances. aws application-autoscaling register-scalable-target \ --service-namespace sagemaker \ --scalable-dimension sagemaker:variant:DesiredInstanceCount \ --resource-id endpoint/my-endpoint/variant/my-variant \ --min-capacity 1 \ --max-capacity 8 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Registering SageMaker AI endpoint variants as scalable targets with Application Auto Scaling 31 Application Auto Scaling User Guide Registering the provisioned concurrency of serverless endpoints as scalable targets with Application Auto Scaling Application Auto Scaling also requires a scalable target before you can create scaling policies or scheduled actions for the provisioned concurrency of serverless endpoints. If you conﬁgure auto scaling using the SageMaker AI console, then SageMaker AI automatically registers a scalable target for you. Otherwise, use one of the following methods to register the scalable target: • AWS CLI: Call the register-scalable-target command for a product variant. The following example registers the provisioned concurrency for a product variant called my-variant, running on the my- endpoint endpoint, with a minimum capacity of one and a maximum capacity of ten. aws application-autoscaling register-scalable-target \ --service-namespace sagemaker \ --scalable-dimension sagemaker:variant:DesiredProvisionedConcurrency \ --resource-id endpoint/my-endpoint/variant/my-variant \ --min-capacity 1 \ --max-capacity 10 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Registering the provisioned concurrency of serverless endpoints as scalable targets with Application Auto Scaling 32 Application Auto Scaling User Guide Registering inference components as scalable targets with Application Auto Scaling Application Auto Scaling also requires a scalable target before you can create scaling policies or scheduled actions for inference components. • AWS CLI: Call the register-scalable-target command for an inference component. The following example registers the desired copy count for an inference component called my-inference-component, with a minimum capacity of zero copies and a maximum capacity of three copies. aws application-autoscaling register-scalable-target \ --service-namespace sagemaker \ --scalable-dimension sagemaker:inference-component:DesiredCopyCount \ --resource-id inference-component/my-inference-component \ --min-capacity 0 \ --max-capacity 3 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources If you are just getting started with Application Auto Scaling, you can ﬁnd additional useful information about scaling your SageMaker AI resources in the Amazon SageMaker AI Developer Guide: • Automatically scale Amazon SageMaker AI models • Automatically scale Provisioned Concurrency for a serverless endpoint Registering inference components as scalable targets with Application Auto Scaling 33 Application Auto Scaling User Guide • Set auto scaling policies for multi-model endpoint deployments • Autoscale an asynchronous endpoint Note In 2023, SageMaker AI introduced new inference capabilities built on real-time inference endpoints. You create a SageMaker AI endpoint with an endpoint conﬁguration that deﬁnes the instance type and initial instance count for the endpoint. Then, create an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint. For information about scaling inference components, see Amazon SageMaker AI adds new inference capabilities to help reduce foundation model deployment costs and latency and Reduce model deployment costs by 50% on average using the latest features of Amazon SageMaker AI on the AWS Blog. Amazon EC2 Spot Fleet and Application Auto Scaling You can scale Spot Fleets using target tracking scaling policies, step scaling policies, and scheduled scaling. Use the following information to help you integrate Spot Fleet with Application Auto Scaling. Service-linked role created for Spot Fleet The following service-linked role is automatically created in your AWS account when registering Spot Fleet resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_EC2SpotFleetRequest Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling
app-as-ug-013	app-as-ug.pdf	13	integrate Spot Fleet with Application Auto Scaling. Service-linked role created for Spot Fleet The following service-linked role is automatically created in your AWS account when registering Spot Fleet resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_EC2SpotFleetRequest Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: Spot Fleet (Amazon EC2) 34 Application Auto Scaling User Guide • ec2.application-autoscaling.amazonaws.com Registering Spot Fleets as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for a Spot Fleet. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. If you conﬁgure auto scaling using the Spot Fleet console, then Spot Fleet automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for a Spot Fleet. The following example registers the target capacity of a Spot Fleet using its request ID, with a minimum capacity of two instances and a maximum capacity of 10 instances. aws application-autoscaling register-scalable-target \ --service-namespace ec2 \ --scalable-dimension ec2:spot-fleet-request:TargetCapacity \ --resource-id spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE \ --min-capacity 2 \ --max-capacity 10 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Registering Spot Fleets as scalable targets with Application Auto Scaling 35 Application Auto Scaling Related resources User Guide For more information, see Understand automatic scaling for Spot Fleet in the Amazon EC2 User Guide. Amazon WorkSpaces and Application Auto Scaling You can scale a pool of WorkSpaces using target tracking scaling policies, step scaling policies, and scheduled scaling. Use the following information to help you integrate WorkSpaces with Application Auto Scaling. Service-linked role created for WorkSpaces Application Auto Scaling automatically creates the service-linked role named AWSServiceRoleForApplicationAutoScaling_WorkSpacesPool in your AWS account when you register WorkSpaces resources as scalable targets with Application Auto Scaling. For more information, see Service-linked roles for Application Auto Scaling. This service-linked role uses the managed policy AWSApplicationAutoscalingWorkSpacesPoolPolicy. This policy grants Application Auto Scaling permissions to call Amazon WorkSpaces on your behalf. For more information, see AWSApplicationAutoscalingWorkSpacesPoolPolicy in the AWS Managed Policy Reference. Service principal used by the service-linked role The service-linked role trusts the following service principal to assume the role: • workspaces.application-autoscaling.amazonaws.com Registering WorkSpaces pools as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for WorkSpaces. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. Related resources 36 Application Auto Scaling User Guide If you conﬁgure auto scaling using the WorkSpaces console, then WorkSpaces automatically registers a scalable target for you. If you want to conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for a pool of WorkSpaces. The following example registers the target capacity of a pool of WorkSpaces using its request ID, with a minimum capacity of two virtual desktops and a maximum capacity of ten virtual desktops. aws application-autoscaling register-scalable-target \ --service-namespace workspaces \ --resource-id workspacespool/wspool-abcdef012 \ --scalable-dimension workspaces:workspacespool:DesiredUserSessions \ --min-capacity 2 \ --max-capacity 10 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources For more information, see Auto Scaling for WorkSpaces Pools in the Amazon WorkSpaces Administration Guide. Custom resources and Application Auto Scaling You can scale custom resources using target tracking scaling policies, step scaling policies, and scheduled scaling. Related resources 37 Application Auto Scaling User Guide Use the following information to help you integrate custom resources with Application Auto Scaling. Service-linked role created for custom resources The following service-linked role is automatically created in your AWS account when registering custom resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_CustomResource Service principal used
app-as-ug-014	app-as-ug.pdf	14	Custom resources and Application Auto Scaling You can scale custom resources using target tracking scaling policies, step scaling policies, and scheduled scaling. Related resources 37 Application Auto Scaling User Guide Use the following information to help you integrate custom resources with Application Auto Scaling. Service-linked role created for custom resources The following service-linked role is automatically created in your AWS account when registering custom resources as scalable targets with Application Auto Scaling. This role allows Application Auto Scaling to perform supported operations within your account. For more information, see Service-linked roles for Application Auto Scaling. • AWSServiceRoleForApplicationAutoScaling_CustomResource Service principal used by the service-linked role The service-linked role in the previous section can be assumed only by the service principal authorized by the trust relationships deﬁned for the role. The service-linked role used by Application Auto Scaling grants access to the following service principal: • custom-resource.application-autoscaling.amazonaws.com Registering custom resources as scalable targets with Application Auto Scaling Application Auto Scaling requires a scalable target before you can create scaling policies or scheduled actions for a custom resource. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Scalable targets are uniquely identiﬁed by the combination of resource ID, scalable dimension, and namespace. To conﬁgure auto scaling using the AWS CLI or one of the AWS SDKs, you can use the following options: • AWS CLI: Call the register-scalable-target command for a custom resource. The following example registers a custom resource as a scalable target, with a minimum desired count of one capacity unit and a maximum desired count of 10 capacity units. The custom-resource-id.txt ﬁle contains a string that identiﬁes the resource ID, which represents the path to the custom resource through your Amazon API Gateway endpoint. Service-linked role 38 Application Auto Scaling User Guide aws application-autoscaling register-scalable-target \ --service-namespace custom-resource \ --scalable-dimension custom-resource:ResourceType:Property \ --resource-id file://~/custom-resource-id.txt \ --min-capacity 1 \ --max-capacity 10 Contents of custom-resource-id.txt: https://example.execute-api.us-west-2.amazonaws.com/prod/ scalableTargetDimensions/1-23456789 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } • AWS SDK: Call the RegisterScalableTarget operation and provide ResourceId, ScalableDimension, ServiceNamespace, MinCapacity, and MaxCapacity as parameters. Related resources If you are just getting started with Application Auto Scaling, you can ﬁnd additional useful information about scaling your custom resources in the following documentation: GitHub repository Related resources 39 Application Auto Scaling User Guide Conﬁgure Application Auto Scaling resources using AWS CloudFormation Application Auto Scaling is integrated with AWS CloudFormation, a service that helps you to model and set up your AWS resources so that you can spend less time creating and managing your resources and infrastructure. You create a template that describes all the AWS resources that you want, and AWS CloudFormation provisions and conﬁgures those resources for you. When you use AWS CloudFormation, you can reuse your template to set up your Application Auto Scaling resources consistently and repeatedly. Describe your resources once, and then provision the same resources over and over in multiple AWS accounts and Regions. Application Auto Scaling and AWS CloudFormation templates To provision and conﬁgure resources for Application Auto Scaling and related services, you must understand AWS CloudFormation templates. Templates are formatted text ﬁles in JSON or YAML. These templates describe the resources that you want to provision in your AWS CloudFormation stacks. If you're unfamiliar with JSON or YAML, you can use AWS CloudFormation Designer to help you get started with AWS CloudFormation templates. For more information, see What is AWS CloudFormation Designer? in the AWS CloudFormation User Guide. When you create a stack template for Application Auto Scaling resources, you must provide the following: • A namespace for the target service (for example, appstream). See the AWS::ApplicationAutoScaling::ScalableTarget reference to obtain service namespaces. • A scalable dimension associated with the target resource (for example, appstream:fleet:DesiredCapacity). See the AWS::ApplicationAutoScaling::ScalableTarget reference to obtain scalable dimensions. • A resource ID for the target resource (for example, fleet/sample-fleet). See the AWS::ApplicationAutoScaling::ScalableTarget reference for information about the syntax and examples of speciﬁc resource IDs. • A service-linked role for the target resource (for example, arn:aws:iam::012345678910:role/aws-service-role/ appstream.application-autoscaling.amazonaws.com/ Application Auto Scaling and AWS CloudFormation templates 40 Application Auto Scaling User Guide AWSServiceRoleForApplicationAutoScaling_AppStreamFleet). See the Service-linked role ARN reference table to obtain role ARNs. To learn more about Application Auto Scaling resources, see the Application Auto Scaling reference in the AWS CloudFormation User Guide. Example template snippets You can ﬁnd example snippets to include in AWS CloudFormation templates in the following sections of the AWS CloudFormation User Guide: • For examples of scaling policies and scheduled actions, see Conﬁgure Application Auto Scaling resources with AWS CloudFormation. • For more examples of scaling policies, see AWS::ApplicationAutoScaling::ScalingPolicy. Learn more about AWS CloudFormation To learn more about AWS CloudFormation, see the following resources: • AWS CloudFormation • AWS CloudFormation User Guide • AWS CloudFormation API Reference
app-as-ug-015	app-as-ug.pdf	15	ARNs. To learn more about Application Auto Scaling resources, see the Application Auto Scaling reference in the AWS CloudFormation User Guide. Example template snippets You can ﬁnd example snippets to include in AWS CloudFormation templates in the following sections of the AWS CloudFormation User Guide: • For examples of scaling policies and scheduled actions, see Conﬁgure Application Auto Scaling resources with AWS CloudFormation. • For more examples of scaling policies, see AWS::ApplicationAutoScaling::ScalingPolicy. Learn more about AWS CloudFormation To learn more about AWS CloudFormation, see the following resources: • AWS CloudFormation • AWS CloudFormation User Guide • AWS CloudFormation API Reference • AWS CloudFormation Command Line Interface User Guide Example template snippets 41 Application Auto Scaling User Guide Scheduled scaling for Application Auto Scaling With scheduled scaling, you can set up automatic scaling for your application based on predictable load changes by creating scheduled actions that increase or decrease capacity at speciﬁc times. This allows you to scale your application proactively to match predictable load changes. For example, let's say you experience a regular weekly traﬃc pattern where load increases mid- week and declines toward the end of the week. You can conﬁgure a scaling schedule in Application Auto Scaling that aligns with this pattern: • On Wednesday morning, one scheduled action increases capacity by increasing the previously set minimum capacity of the scalable target. • On Friday evening, another scheduled action decreases capacity by decreasing the previously set maximum capacity of the scalable target. These scheduled scaling actions allow you to optimize costs and performance. Your application has suﬃcient capacity to handle the mid-week traﬃc peak, but does not over-provision unneeded capacity at other times. You can use scheduled scaling and scaling policies together to get the beneﬁts of proactive and reactive approaches to scaling. After a scheduled scaling action runs, the scaling policy can continue to make decisions about whether to further scale capacity. This helps you ensure that you have suﬃcient capacity to handle the load for your application. While your application scales to match demand, current capacity must fall within the minimum and maximum capacity that was set by your scheduled action. Contents • How scheduled scaling for Application Auto Scaling works • Create scheduled actions for Application Auto Scaling using the AWS CLI • Describe scheduled scaling for Application Auto Scaling using the AWS CLI • Schedule recurring scaling actions using Application Auto Scaling • Turn oﬀ scheduled scaling for a scalable target • Delete a scheduled action for Application Auto Scaling using the AWS CLI 42 Application Auto Scaling User Guide How scheduled scaling for Application Auto Scaling works This topic describes how scheduled scaling works and introduces the key considerations you need to understand to use it eﬀectively. Contents • How it works • Considerations • Commonly used commands for scheduled action creation, management, and deletion • Related resources • Limitations How it works To use scheduled scaling, create scheduled actions, which tell Application Auto Scaling to perform scaling activities at speciﬁc times. When you create a scheduled action, you specify the scalable target, when the scaling activity should occur, a minimum capacity, and a maximum capacity. You can create scheduled actions that scale one time only or that scale on a recurring schedule. At the speciﬁed time, Application Auto Scaling scales based on the new capacity values, by comparing current capacity to the speciﬁed minimum and maximum capacity. • If current capacity is less than the speciﬁed minimum capacity, Application Auto Scaling scales out (increases capacity) to the speciﬁed minimum capacity. • If current capacity is greater than the speciﬁed maximum capacity, Application Auto Scaling scales in (decreases capacity) to the speciﬁed maximum capacity. Considerations When you create a scheduled action, keep the following in mind: • A scheduled action sets the MinCapacity and MaxCapacity to what is speciﬁed by the scheduled action at the date and time speciﬁed. The request can optionally include only one of these sizes. For example, you can create a scheduled action with only the minimum capacity speciﬁed. In some cases, however, you must include both sizes to ensure that the new minimum How scheduled scaling works 43 Application Auto Scaling User Guide capacity is not greater than the maximum capacity, or the new maximum capacity is not less than the minimum capacity. • By default, the recurring schedules that you set are in Coordinated Universal Time (UTC). You can change the time zone to correspond to your local time zone or a time zone for another part of your network. When you specify a time zone that observes daylight saving time, the action automatically adjusts for Daylight Saving Time (DST). For more information, see Schedule recurring scaling actions using Application Auto Scaling. • You can temporarily turn oﬀ scheduled scaling for a scalable target. This helps you
app-as-ug-016	app-as-ug.pdf	16	the maximum capacity, or the new maximum capacity is not less than the minimum capacity. • By default, the recurring schedules that you set are in Coordinated Universal Time (UTC). You can change the time zone to correspond to your local time zone or a time zone for another part of your network. When you specify a time zone that observes daylight saving time, the action automatically adjusts for Daylight Saving Time (DST). For more information, see Schedule recurring scaling actions using Application Auto Scaling. • You can temporarily turn oﬀ scheduled scaling for a scalable target. This helps you prevent scheduled actions from being active without having to delete them. You can then resume scheduled scaling when you want to use it again. For more information, see Suspend and resume scaling for Application Auto Scaling. • The order in which scheduled actions run is guaranteed for the same scalable target, but not for scheduled actions across scalable targets. • To complete a scheduled action successfully, the speciﬁed resource must be in a scalable state in the target service. If it isn't, the request fails and returns an error message, for example, Resource Id [ActualResourceId] is not scalable. Reason: The status of all DB instances must be 'available' or 'incompatible-parameters'. • Due to the distributed nature of Application Auto Scaling and the target services, the delay between the time the scheduled action is triggered and the time the target service honors the scaling action might be a few seconds. Because scheduled actions are run in the order that they are speciﬁed, scheduled actions with start times close to each other can take longer to run. Commonly used commands for scheduled action creation, management, and deletion The commonly used commands for working with schedule scaling include: • register-scalable-target to register AWS or custom resources as scalable targets (a resource that Application Auto Scaling can scale), and to suspend and resume scaling. • put-scheduled-action to add or modify scheduled actions for an existing scalable target. • describe-scaling-activities to return information about scaling activities in an AWS Region. • describe-scheduled-actions to return information about scheduled actions in an AWS Region. • delete-scheduled-action to delete a scheduled action. Commonly used commands 44 Application Auto Scaling Related resources User Guide For a detailed example of using scheduled scaling, see the blog post Scheduling AWS Lambda Provisioned Concurrency for recurring peak usage on the AWS Compute Blog. For information about creating scheduled actions for Auto Scaling groups, see Scheduled scaling for Amazon EC2 Auto Scaling in the Amazon EC2 Auto Scaling User Guide. Limitations The following are limitations when using scheduled scaling: • The names of scheduled actions must be unique per scalable target. • Application Auto Scaling doesn't provide second-level precision in schedule expressions. The ﬁnest resolution using a cron expression is 1 minute. • The scalable target can't be an Amazon MSK cluster. Scheduled scaling is not supported for Amazon MSK. • Console access to view, add, update, or remove scheduled actions on scalable resources depends on the resource that you use. For more information, see AWS services that you can use with Application Auto Scaling. Create scheduled actions for Application Auto Scaling using the AWS CLI The following examples show how to create scheduled actions using the AWS CLI put-scheduled- action command. When you specify the new capacity, you can specify a minimum capacity, a maximum capacity, or both. These examples use scalable targets for a few of the services that integrate with Application Auto Scaling. To use a diﬀerent scalable target, specify its namespace in --service-namespace, its scalable dimension in --scalable-dimension, and its resource ID in --resource-id. When using the AWS CLI, remember that your commands run in the AWS Region conﬁgured for your proﬁle. If you want to run the commands in a diﬀerent Region, either change the default Region for your proﬁle, or use the --region parameter with the command. Examples • Create a scheduled action that occurs only once Related resources 45 Application Auto Scaling User Guide • Create a scheduled action that runs on a recurring interval • Create a scheduled action that runs on a recurring schedule • Create a one-time scheduled action that speciﬁes a time zone • Create a recurring scheduled action that speciﬁes a time zone Create a scheduled action that occurs only once To automatically scale your scalable target one time only, at a speciﬁed date and time, use the -- schedule "at(yyyy-mm-ddThh:mm:ss)" option. Example Example: To scale out one time only The following is an example of creating a scheduled action to scale out capacity at a speciﬁc date and time. At the date and time speciﬁed for --schedule (10:00 PM UTC on March 31, 2021), if the value speciﬁed for MinCapacity is above the current capacity, Application Auto Scaling scales out
app-as-ug-017	app-as-ug.pdf	17	zone • Create a recurring scheduled action that speciﬁes a time zone Create a scheduled action that occurs only once To automatically scale your scalable target one time only, at a speciﬁed date and time, use the -- schedule "at(yyyy-mm-ddThh:mm:ss)" option. Example Example: To scale out one time only The following is an example of creating a scheduled action to scale out capacity at a speciﬁc date and time. At the date and time speciﬁed for --schedule (10:00 PM UTC on March 31, 2021), if the value speciﬁed for MinCapacity is above the current capacity, Application Auto Scaling scales out to MinCapacity. Linux, macOS, or Unix aws application-autoscaling put-scheduled-action --service-namespace custom-resource \ --scalable-dimension custom-resource:ResourceType:Property \ --resource-id file://~/custom-resource-id.txt \ --scheduled-action-name scale-out \ --schedule "at(2021-03-31T22:00:00)" \ --scalable-target-action MinCapacity=3 Windows aws application-autoscaling put-scheduled-action --service-namespace custom-resource ^ --scalable-dimension custom-resource:ResourceType:Property ^ --resource-id file://~/custom-resource-id.txt ^ --scheduled-action-name scale-out ^ --schedule "at(2021-03-31T22:00:00)" ^ --scalable-target-action MinCapacity=3 When this scheduled action runs, if the maximum capacity is less than the value speciﬁed for minimum capacity, you must specify a new minimum and maximum capacity, and not just the minimum capacity. Create a scheduled action that occurs only once 46 Application Auto Scaling User Guide Example Example: To scale in one time only The following is an example of creating a scheduled action to scale in capacity at a speciﬁc date and time. At the date and time speciﬁed for --schedule (10:30 PM UTC on March 31, 2021), if the value speciﬁed for MaxCapacity is below the current capacity, Application Auto Scaling scales in to MaxCapacity. Linux, macOS, or Unix aws application-autoscaling put-scheduled-action --service-namespace custom-resource \ --scalable-dimension custom-resource:ResourceType:Property \ --resource-id file://~/custom-resource-id.txt \ --scheduled-action-name scale-in \ --schedule "at(2021-03-31T22:30:00)" \ --scalable-target-action MinCapacity=0,MaxCapacity=0 Windows aws application-autoscaling put-scheduled-action --service-namespace custom-resource ^ --scalable-dimension custom-resource:ResourceType:Property ^ --resource-id file://~/custom-resource-id.txt ^ --scheduled-action-name scale-in ^ --schedule "at(2021-03-31T22:30:00)" ^ --scalable-target-action MinCapacity=0,MaxCapacity=0 Create a scheduled action that runs on a recurring interval To schedule scaling at a recurring interval, use the --schedule "rate(value unit)" option. The value must be a positive integer. The unit can be minute, minutes, hour, hours, day, or days. For more information, see Rate expressions in the Amazon EventBridge User Guide. The following is an example of a scheduled action that uses a rate expression. On the speciﬁed schedule (every 5 hours starting on January 30, 2021 at 12:00 PM UTC and ending on January 31, 2021 at 10:00 PM UTC), if the value speciﬁed for MinCapacity is above the current capacity, Application Auto Scaling scales out to MinCapacity. If the value speciﬁed for MaxCapacity is below the current capacity, Application Auto Scaling scales in to MaxCapacity. Linux, macOS, or Unix Create a scheduled action that runs on a recurring interval 47 Application Auto Scaling User Guide aws application-autoscaling put-scheduled-action --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id service/my-cluster/my-service \ --scheduled-action-name my-recurring-action \ --schedule "rate(5 hours)" \ --start-time 2021-01-30T12:00:00 \ --end-time 2021-01-31T22:00:00 \ --scalable-target-action MinCapacity=3,MaxCapacity=10 Windows aws application-autoscaling put-scheduled-action --service-namespace ecs ^ --scalable-dimension ecs:service:DesiredCount ^ --resource-id service/my-cluster/my-service ^ --scheduled-action-name my-recurring-action ^ --schedule "rate(5 hours)" ^ --start-time 2021-01-30T12:00:00 ^ --end-time 2021-01-31T22:00:00 ^ --scalable-target-action MinCapacity=3,MaxCapacity=10 Create a scheduled action that runs on a recurring schedule To schedule scaling on a recurring schedule, use the --schedule "cron(fields)" option. For more information, see Schedule recurring scaling actions using Application Auto Scaling. The following is an example of a scheduled action that uses a cron expression. On the speciﬁed schedule (every day at 9:00 AM UTC), if the value speciﬁed for MinCapacity is above the current capacity, Application Auto Scaling scales out to MinCapacity. If the value speciﬁed for MaxCapacity is below the current capacity, Application Auto Scaling scales in to MaxCapacity. Linux, macOS, or Unix aws application-autoscaling put-scheduled-action --service-namespace appstream \ --scalable-dimension appstream:fleet:DesiredCapacity \ --resource-id fleet/sample-fleet \ --scheduled-action-name my-recurring-action \ --schedule "cron(0 9 * * ? )" \ --scalable-target-action MinCapacity=10,MaxCapacity=50 Create a scheduled action that runs on a recurring schedule 48 Application Auto Scaling Windows User Guide aws application-autoscaling put-scheduled-action --service-namespace appstream ^ --scalable-dimension appstream:fleet:DesiredCapacity ^ --resource-id fleet/sample-fleet ^ --scheduled-action-name my-recurring-action ^ --schedule "cron(0 9 * ? *)" ^ --scalable-target-action MinCapacity=10,MaxCapacity=50 Create a one-time scheduled action that speciﬁes a time zone Scheduled actions are set to the UTC time zone by default. To specify a diﬀerent time zone, include the --timezone option and specify the canonical name for the time zone (America/New_York, for example). For more information, see https://www.joda.org/joda-time/timezones.html, which provides information about the IANA time zones that are supported when calling put-scheduled- action. The following is an example that uses the --timezone option when creating a scheduled action to scale capacity at a speciﬁc date and time. At the date and time speciﬁed for --schedule (5:00 PM local time on January 31, 2021), if the value speciﬁed for MinCapacity is above the current capacity, Application Auto Scaling scales out to MinCapacity. If the value speciﬁed for MaxCapacity is below the current
app-as-ug-018	app-as-ug.pdf	18	and specify the canonical name for the time zone (America/New_York, for example). For more information, see https://www.joda.org/joda-time/timezones.html, which provides information about the IANA time zones that are supported when calling put-scheduled- action. The following is an example that uses the --timezone option when creating a scheduled action to scale capacity at a speciﬁc date and time. At the date and time speciﬁed for --schedule (5:00 PM local time on January 31, 2021), if the value speciﬁed for MinCapacity is above the current capacity, Application Auto Scaling scales out to MinCapacity. If the value speciﬁed for MaxCapacity is below the current capacity, Application Auto Scaling scales in to MaxCapacity. Linux, macOS, or Unix aws application-autoscaling put-scheduled-action --service-namespace comprehend \ --scalable-dimension comprehend:document-classifier-endpoint:DesiredInferenceUnits \ --resource-id arn:aws:comprehend:us-west-2:123456789012:document-classifier-endpoint/ EXAMPLE \ --scheduled-action-name my-one-time-action \ --schedule "at(2021-01-31T17:00:00)" --timezone "America/New_York" \ --scalable-target-action MinCapacity=1,MaxCapacity=3 Windows aws application-autoscaling put-scheduled-action --service-namespace comprehend ^ --scalable-dimension comprehend:document-classifier-endpoint:DesiredInferenceUnits ^ --resource-id arn:aws:comprehend:us-west-2:123456789012:document-classifier-endpoint/ EXAMPLE ^ Create a one-time scheduled action that speciﬁes a time zone 49 Application Auto Scaling User Guide --scheduled-action-name my-one-time-action ^ --schedule "at(2021-01-31T17:00:00)" --timezone "America/New_York" ^ --scalable-target-action MinCapacity=1,MaxCapacity=3 Create a recurring scheduled action that speciﬁes a time zone The following is an example that uses the --timezone option when creating a recurring scheduled action to scale capacity. For more information, see Schedule recurring scaling actions using Application Auto Scaling. On the speciﬁed schedule (every Monday through Friday at 6:00 PM local time), if the value speciﬁed for MinCapacity is above the current capacity, Application Auto Scaling scales out to MinCapacity. If the value speciﬁed for MaxCapacity is below the current capacity, Application Auto Scaling scales in to MaxCapacity. Linux, macOS, or Unix aws application-autoscaling put-scheduled-action --service-namespace lambda \ --scalable-dimension lambda:function:ProvisionedConcurrency \ --resource-id function:my-function:BLUE \ --scheduled-action-name my-recurring-action \ --schedule "cron(0 18 ? * MON-FRI )" --timezone "Etc/GMT+9" \ --scalable-target-action MinCapacity=10,MaxCapacity=50 Windows aws application-autoscaling put-scheduled-action --service-namespace lambda ^ --scalable-dimension lambda:function:ProvisionedConcurrency ^ --resource-id function:my-function:BLUE ^ --scheduled-action-name my-recurring-action ^ --schedule "cron(0 18 ? MON-FRI *)" --timezone "Etc/GMT+9" ^ --scalable-target-action MinCapacity=10,MaxCapacity=50 Describe scheduled scaling for Application Auto Scaling using the AWS CLI These example AWS CLI commands describe scaling activities and scheduled actions using resources from services that integrate with Application Auto Scaling. For a diﬀerent scalable Create a recurring scheduled action that speciﬁes a time zone 50 Application Auto Scaling User Guide target, specify its namespace in --service-namespace, its scalable dimension in --scalable- dimension, and its resource ID in --resource-id. When using the AWS CLI, remember that your commands run in the AWS Region conﬁgured for your proﬁle. If you want to run the commands in a diﬀerent Region, either change the default Region for your proﬁle, or use the --region parameter with the command. Examples • Describe scaling activities for a service • Describe the scheduled actions for a service • Describe the scheduled actions for a scalable target Describe scaling activities for a service To view the scaling activities for all of the scalable targets in a speciﬁed service namespace, use the describe-scaling-activities command. The following example retrieves the scaling activities associated with the dynamodb service namespace. Linux, macOS, or Unix aws application-autoscaling describe-scaling-activities --service-namespace dynamodb Windows aws application-autoscaling describe-scaling-activities --service-namespace dynamodb Output If the command succeeds, it returns output similar to the following. { "ScalingActivities": [ { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting write capacity units to 10.", "ResourceId": "table/my-table", "ActivityId": "4d1308c0-bbcf-4514-a673-b0220ae38547", "StartTime": 1561574415.086, Describe scaling activities for a service 51 Application Auto Scaling User Guide "ServiceNamespace": "dynamodb", "EndTime": 1561574449.51, "Cause": "maximum capacity was set to 10", "StatusMessage": "Successfully set write capacity units to 10. Change successfully fulfilled by dynamodb.", "StatusCode": "Successful" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting min capacity to 5 and max capacity to 10", "ResourceId": "table/my-table", "ActivityId": "f2b7847b-721d-4e01-8ef0-0c8d3bacc1c7", "StartTime": 1561574414.644, "ServiceNamespace": "dynamodb", "Cause": "scheduled action name my-second-scheduled-action was triggered", "StatusMessage": "Successfully set min capacity to 5 and max capacity to 10", "StatusCode": "Successful" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting write capacity units to 15.", "ResourceId": "table/my-table", "ActivityId": "d8ea4de6-9eaa-499f-b466-2cc5e681ba8b", "StartTime": 1561574108.904, "ServiceNamespace": "dynamodb", "EndTime": 1561574140.255, "Cause": "minimum capacity was set to 15", "StatusMessage": "Successfully set write capacity units to 15. Change successfully fulfilled by dynamodb.", "StatusCode": "Successful" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting min capacity to 15 and max capacity to 20", "ResourceId": "table/my-table", "ActivityId": "3250fd06-6940-4e8e-bb1f-d494db7554d2", "StartTime": 1561574108.512, "ServiceNamespace": "dynamodb", "Cause": "scheduled action name my-first-scheduled-action was triggered", "StatusMessage": "Successfully set min capacity to 15 and max capacity to 20", "StatusCode": "Successful" } Describe scaling activities for a service 52 Application Auto Scaling ] } User Guide To change this command so that it retrieves the scaling activities for only one of your scalable targets, add the --resource-id option. Describe the scheduled actions for a service To describe the scheduled actions for all of the scalable targets in a speciﬁed service namespace, use the describe-scheduled-actions command. The following example retrieves the scheduled actions associated with the ec2 service namespace. Linux, macOS, or Unix aws
app-as-ug-019	app-as-ug.pdf	19	name my-first-scheduled-action was triggered", "StatusMessage": "Successfully set min capacity to 15 and max capacity to 20", "StatusCode": "Successful" } Describe scaling activities for a service 52 Application Auto Scaling ] } User Guide To change this command so that it retrieves the scaling activities for only one of your scalable targets, add the --resource-id option. Describe the scheduled actions for a service To describe the scheduled actions for all of the scalable targets in a speciﬁed service namespace, use the describe-scheduled-actions command. The following example retrieves the scheduled actions associated with the ec2 service namespace. Linux, macOS, or Unix aws application-autoscaling describe-scheduled-actions --service-namespace ec2 Windows aws application-autoscaling describe-scheduled-actions --service-namespace ec2 Output If the command succeeds, it returns output similar to the following. { "ScheduledActions": [ { "ScheduledActionName": "my-one-time-action", "ScheduledActionARN": "arn:aws:autoscaling:us- west-2:123456789012:scheduledAction:493a6261-fbb9-432d-855d-3c302c14bdb9:resource/ec2/ spot-fleet-request/sfr-107dc873-0802-4402-a901-37294EXAMPLE:scheduledActionName/my-one- time-action", "ServiceNamespace": "ec2", "Schedule": "at(2021-01-31T17:00:00)", "Timezone": "America/New_York", "ResourceId": "spot-fleet-request/sfr-107dc873-0802-4402- a901-37294EXAMPLE", "ScalableDimension": "ec2:spot-fleet-request:TargetCapacity", "ScalableTargetAction": { "MaxCapacity": 1 }, Describe the scheduled actions for a service 53 Application Auto Scaling User Guide "CreationTime": 1607454792.331 }, { "ScheduledActionName": "my-recurring-action", "ScheduledActionARN": "arn:aws:autoscaling:us- west-2:123456789012:scheduledAction:493a6261-fbb9-432d-855d-3c302c14bdb9:resource/ec2/ spot-fleet-request/sfr-107dc873-0802-4402-a901-37294EXAMPLE:scheduledActionName/my- recurring-action", "ServiceNamespace": "ec2", "Schedule": "rate(5 minutes)", "ResourceId": "spot-fleet-request/sfr-107dc873-0802-4402- a901-37294EXAMPLE", "ScalableDimension": "ec2:spot-fleet-request:TargetCapacity", "StartTime": 1604059200.0, "EndTime": 1612130400.0, "ScalableTargetAction": { "MinCapacity": 3, "MaxCapacity": 10 }, "CreationTime": 1607454949.719 }, { "ScheduledActionName": "my-one-time-action", "ScheduledActionARN": "arn:aws:autoscaling:us- west-2:123456789012:scheduledAction:4bce34c7-bb81-4ecf-b776-5c726efb1567:resource/ec2/ spot-fleet-request/sfr-40edeb7b-9ae7-44be-bef2-5c4c8EXAMPLE:scheduledActionName/my-one- time-action", "ServiceNamespace": "ec2", "Schedule": "at(2020-12-08T9:36:00)", "Timezone": "America/New_York", "ResourceId": "spot-fleet-request/sfr-40edeb7b-9ae7-44be- bef2-5c4c8EXAMPLE", "ScalableDimension": "ec2:spot-fleet-request:TargetCapacity", "ScalableTargetAction": { "MinCapacity": 1, "MaxCapacity": 3 }, "CreationTime": 1607456031.391 } ] } Describe the scheduled actions for a service 54 Application Auto Scaling User Guide Describe the scheduled actions for a scalable target To retrieve information about the scheduled actions for a speciﬁed scalable target, add the -- resource-id option when describing scheduled actions using the describe-scheduled-actions command. If you include the --scheduled-action-names option and specify the name of a scheduled action as its value, the command returns only the scheduled action whose name is a match, as shown in the following example. Linux, macOS, or Unix aws application-autoscaling describe-scheduled-actions --service-namespace ec2 \ --resource-id spot-fleet-request/sfr-40edeb7b-9ae7-44be-bef2-5c4c8EXAMPLE \ --scheduled-action-names my-one-time-action Windows aws application-autoscaling describe-scheduled-actions --service-namespace ec2 ^ --resource-id spot-fleet-request/sfr-40edeb7b-9ae7-44be-bef2-5c4c8EXAMPLE ^ --scheduled-action-names my-one-time-action Output If the command succeeds, it returns output similar to the following. If you provided more than one value for --scheduled-action-names, the output includes all scheduled actions whose names are a match. { "ScheduledActions": [ { "ScheduledActionName": "my-one-time-action", "ScheduledActionARN": "arn:aws:autoscaling:us- west-2:123456789012:scheduledAction:4bce34c7-bb81-4ecf-b776-5c726efb1567:resource/ec2/ spot-fleet-request/sfr-40edeb7b-9ae7-44be-bef2-5c4c8EXAMPLE:scheduledActionName/my-one- time-action", "ServiceNamespace": "ec2", "Schedule": "at(2020-12-08T9:36:00)", "Timezone": "America/New_York", "ResourceId": "spot-fleet-request/sfr-40edeb7b-9ae7-44be- bef2-5c4c8EXAMPLE", Describe the scheduled actions for a scalable target 55 Application Auto Scaling User Guide "ScalableDimension": "ec2:spot-fleet-request:TargetCapacity", "ScalableTargetAction": { "MinCapacity": 1, "MaxCapacity": 3 }, "CreationTime": 1607456031.391 } ] } Schedule recurring scaling actions using Application Auto Scaling Important For help with cron expressions for Amazon EC2 Auto Scaling, see the Recurring schedules topic in the Amazon EC2 Auto Scaling User Guide. With Amazon EC2 Auto Scaling, you use traditional cron syntax instead of the custom cron syntax that Application Auto Scaling uses. You can create scheduled actions that run on a recurring schedule using a cron expression. To create a recurring schedule, specify a cron expression and a time zone to describe when that scheduled action is to recur. The supported time zone values are the canonical names of the IANA time zones supported by Joda-Time (such as Etc/GMT+9 or Pacific/Tahiti). You can optionally specify a date and time for the start time, the end time, or both. For an example command that uses the AWS CLI to create a scheduled action, see Create a recurring scheduled action that speciﬁes a time zone. The supported cron expression format consists of six ﬁelds separated by white spaces: [Minutes] [Hours] [Day_of_Month] [Month] [Day_of_Week] [Year]. For example, the cron expression 30 6 ? * MON * conﬁgures a scheduled action that recurs every Monday at 6:30 AM. The asterisk is used as a wildcard to match all values for a ﬁeld. For more information about the cron syntax for Application Auto Scaling scheduled actions, see Cron expressions reference in the Amazon EventBridge User Guide. Schedule recurring scaling actions 56 Application Auto Scaling User Guide When you create a recurring schedule, choose your start and end times carefully. Keep the following in mind: • If you specify a start time, Application Auto Scaling performs the action at this time, and then performs the action based on the speciﬁed recurrence. • If you specify an end time, the action stops repeating after this time. Application Auto Scaling does not keep track of previous values and revert back to those previous values after the end time. • The start time and end time must be set in UTC when you use the AWS CLI or the AWS SDKs to create or update a scheduled action. Examples You can refer to the following table when you create a recurring schedule for an Application Auto Scaling scalable target. The following examples are the correct syntax for using Application Auto Scaling to
app-as-ug-020	app-as-ug.pdf	20	speciﬁed recurrence. • If you specify an end time, the action stops repeating after this time. Application Auto Scaling does not keep track of previous values and revert back to those previous values after the end time. • The start time and end time must be set in UTC when you use the AWS CLI or the AWS SDKs to create or update a scheduled action. Examples You can refer to the following table when you create a recurring schedule for an Application Auto Scaling scalable target. The following examples are the correct syntax for using Application Auto Scaling to create or update a scheduled action. Minutes Hours Day of month Month Day of week Year Meaning 0 10 15 12 0 18 * * ? * * * ? ? * * MON-FRI * Run at 10:00 am (UTC) every day Run at 12:15 pm (UTC) every day Run at 6:00 pm (UTC) every Monday through Friday Schedule recurring scaling actions 57 Application Auto Scaling Minutes Hours Day of month Month Day of week Year Meaning User Guide 0 8 1 0/15 0/10 * * 0/5 8-17 * ? ? * * * * ? ? MON-FRI * * * MON-FRI * Run at 8:00 am (UTC) the 1st day of every month Run every 15 minutes Run every 10 minutes Monday through Friday Run every 5 minutes Monday through Friday between 8:00 am and 5:55 pm (UTC) Exception You can also create a cron expression with a string value that contains seven ﬁelds. In this case, you can use the ﬁrst three ﬁelds to specify the time for when a scheduled action should be run, including the seconds. The full cron expression has the following space-separated ﬁelds: [Seconds] [Minutes] [Hours] [Day_of_Month] [Month] [Day_of_Week] [Year]. However, this approach doesn't guarantee that the scheduled action will run on the precise second that you specify. Also, some service consoles may not support the seconds ﬁeld in a cron expression. Schedule recurring scaling actions 58 Application Auto Scaling User Guide Turn oﬀ scheduled scaling for a scalable target You can temporarily turn oﬀ scheduled scaling without deleting your scheduled actions. For more information, see Suspend and resume scaling for Application Auto Scaling. To suspend scheduled scaling Suspend scheduled scaling on a scalable target by using the register-scalable-target command with the --suspended-state option, and specifying true as the value of the ScheduledScalingSuspended attribute, as shown in the following example. Linux, macOS, or Unix aws application-autoscaling register-scalable-target --service-namespace rds \ --scalable-dimension rds:cluster:ReadReplicaCount --resource-id cluster:my-db-cluster \ --suspended-state '{"ScheduledScalingSuspended": true}' Windows aws application-autoscaling register-scalable-target --service-namespace rds ^ --scalable-dimension rds:cluster:ReadReplicaCount --resource-id cluster:my-db-cluster ^ --suspended-state "{\"ScheduledScalingSuspended\": true}" Output If the command succeeds, it returns the ARN of the scalable target. The following is example output. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } To resume scheduled scaling To resume scheduled scaling, run the register-scalable-target command again, specifying false as the value for ScheduledScalingSuspended. Turn oﬀ scheduled scaling 59 Application Auto Scaling User Guide Delete a scheduled action for Application Auto Scaling using the AWS CLI When you are ﬁnished with a scheduled action, you can delete it. To delete your scheduled action Use the delete-scheduled-action command. If successful, this command does not return any output. Linux, macOS, or Unix aws application-autoscaling delete-scheduled-action \ --service-namespace ec2 \ --scalable-dimension ec2:spot-fleet-request:TargetCapacity \ --resource-id spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-37294EXAMPLE \ --scheduled-action-name my-recurring-action Windows aws application-autoscaling delete-scheduled-action ^ --service-namespace ec2 ^ --scalable-dimension ec2:spot-fleet-request:TargetCapacity ^ --resource-id spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-37294EXAMPLE ^ --scheduled-action-name my-recurring-action To deregister the scalable target If you are also ﬁnished with the scalable target, you can deregister it. Use the following deregister- scalable-target command. If there are any scaling policies or scheduled actions that have not yet been deleted, they are deleted by this command. If successful, this command does not return any output. Linux, macOS, or Unix aws application-autoscaling deregister-scalable-target \ --service-namespace ec2 \ --scalable-dimension ec2:spot-fleet-request:TargetCapacity \ --resource-id spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-37294EXAMPLE Delete a scheduled action 60 Application Auto Scaling Windows User Guide aws application-autoscaling deregister-scalable-target ^ --service-namespace ec2 ^ --scalable-dimension ec2:spot-fleet-request:TargetCapacity ^ --resource-id spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-37294EXAMPLE Delete a scheduled action 61 Application Auto Scaling User Guide Target tracking scaling policies for Application Auto Scaling A target tracking scaling policy automatically scales your application based on a target metric value. This allows your application to maintain optimal performance and cost eﬃciency without manual intervention. With target tracking, you select a metric and a target value to represent the ideal average utilization or throughput level for your application. Application Auto Scaling creates and manages the CloudWatch alarms that trigger scaling events when the metric deviates from the target. This is similar to how a thermostat maintains a target temperature. For example, let's say that you currently have an application that runs on Spot Fleet, and you want the CPU utilization of the ﬂeet to stay at around 50 percent when the load on the application changes.
app-as-ug-021	app-as-ug.pdf	21	maintain optimal performance and cost eﬃciency without manual intervention. With target tracking, you select a metric and a target value to represent the ideal average utilization or throughput level for your application. Application Auto Scaling creates and manages the CloudWatch alarms that trigger scaling events when the metric deviates from the target. This is similar to how a thermostat maintains a target temperature. For example, let's say that you currently have an application that runs on Spot Fleet, and you want the CPU utilization of the ﬂeet to stay at around 50 percent when the load on the application changes. This gives you extra capacity to handle traﬃc spikes without maintaining an excessive number of idle resources. You can meet this need by creating a target tracking scaling policy that targets an average CPU utilization of 50 percent. Then, Application Auto Scaling will scale out (increase capacity) when CPU exceeds 50 percent to handle increased load. It will scale in (decrease capacity) when CPU drops below 50 percent to optimize costs during periods of low utilization. Target tracking policies remove the need to manually deﬁne CloudWatch alarms and scaling adjustments. Application Auto Scaling handles this automatically based on the target you set. You can base target tracking policies on either predeﬁned or custom metrics: • Predeﬁned metrics—Metrics provided by Application Auto Scaling like average CPU utilization or average request count per target. • Custom metrics—You can use metric math to combine metrics, leverage existing metrics, or use your own custom metrics published to CloudWatch. Choose a metric that changes inversely proportional to a change in the capacity of your scalable target. So if you double capacity, the metric decreases by 50 percent. This allows the metric data to accurately trigger proportional scaling events. Contents 62 Application Auto Scaling User Guide • How target tracking scaling for Application Auto Scaling works • Create a target tracking scaling policy for Application Auto Scaling using the AWS CLI • Delete a target tracking scaling policy for Application Auto Scaling using the AWS CLI • Create a target tracking scaling policy for Application Auto Scaling using metric math How target tracking scaling for Application Auto Scaling works This topic describes how target tracking scaling works and introduces the key elements of a target tracking scaling policy. Contents • How it works • Choose metrics • Deﬁne target value • Deﬁne cooldown periods • Considerations • Multiple scaling policies • Commonly used commands for scaling policy creation, management, and deletion • Related resources • Limitations How it works To use target tracking scaling, you create a target tracking scaling policy and specify the following: • Metric—A CloudWatch metric to track, such as average CPU utilization or average request count per target. • Target value—The target value for the metric, such as 50 percent CPU utilization or 1000 requests per target per minute. Application Auto Scaling creates and manages the CloudWatch alarms that invoke the scaling policy and calculates the scaling adjustment based on the metric and the target value. It adds and removes capacity as required to keep the metric at, or close to, the speciﬁed target value. How target tracking works 63 Application Auto Scaling User Guide When the metric is above the target value, Application Auto Scaling scales out by adding capacity to reduce the diﬀerence between the metric value and the target value. When the metric is below the target value, Application Auto Scaling scales in by removing capacity. Scaling activities are performed with cooldown periods between them to prevent rapid ﬂuctuations in capacity. You can optionally conﬁgure the cooldown periods for your scaling policy. The following diagram shows an overview of how a target tracking scaling policy works when the set up is complete. Note that a target tracking scaling policy is more aggressive in adding capacity when utilization increases than it is in removing capacity when utilization decreases. For example, if the policy's speciﬁed metric reaches its target value, the policy assumes that your application is already heavily loaded. So it responds by adding capacity proportional to the metric value as fast as it can. The higher the metric, the more capacity is added. When the metric falls below the target value, the policy expects that utilization will eventually increase again. In this case, it slows down scaling by removing capacity only when utilization passes a threshold that is far enough below the target value (usually more than 10% lower) for utilization to be considered to have slowed. The intention of this more conservative behavior is to ensure that removing capacity only happens when the application is no longer experiencing demand at the same high level that it was previously. Choose metrics You can create target tracking scaling policies with either predeﬁned metrics or custom metrics. When you create a
app-as-ug-022	app-as-ug.pdf	22	target value, the policy expects that utilization will eventually increase again. In this case, it slows down scaling by removing capacity only when utilization passes a threshold that is far enough below the target value (usually more than 10% lower) for utilization to be considered to have slowed. The intention of this more conservative behavior is to ensure that removing capacity only happens when the application is no longer experiencing demand at the same high level that it was previously. Choose metrics You can create target tracking scaling policies with either predeﬁned metrics or custom metrics. When you create a target tracking scaling policy with a predeﬁned metric type, you choose one metric from the list of predeﬁned metrics in Predeﬁned metrics for target tracking scaling policies. Keep the following in mind when choosing a metric: Choose metrics 64 Application Auto Scaling User Guide • Not all custom metrics work for target tracking. The metric must be a valid utilization metric and describe how busy a scalable target is. The metric value must increase or decrease proportionally to the capacity of the scalable target so that the metric data can be used to proportionally scale the scalable target. • To use the ALBRequestCountPerTarget metric, you must specify the ResourceLabel parameter to identify the target group that is associated with the metric. • When a metric emits real 0 values to CloudWatch (for example, ALBRequestCountPerTarget), Application Auto Scaling can scale in to 0 when there is no traﬃc to your application for a sustained period of time. To have your scalable target scale in to 0 when no requests are routed it, the scalable target's minimum capacity must be set to 0. • Instead of publishing new metrics to use in your scaling policy, you can use metric math to combine existing metrics. For more information, see Create a target tracking scaling policy for Application Auto Scaling using metric math. • To see whether the service you are using supports specifying a custom metric in the service's console, consult the documentation for that service. • We recommend that you use metrics that are available at one-minute intervals to help you scale faster in response to utilization changes. Target tracking will evaluate metrics aggregated at a one-minute granularity for all predeﬁned metrics and custom metrics, but the underlying metric might publish data less frequently. For example, all Amazon EC2 metrics are sent in ﬁve-minute intervals by default, but they are conﬁgurable to one minute (known as detailed monitoring). This choice is up to the individual services. Most try to use the smallest interval possible. Deﬁne target value When you create a target tracking scaling policy, you must specify a target value. The target value represents the optimal average utilization or throughput for your application. To use resources cost eﬃciently, set the target value as high as possible with a reasonable buﬀer for unexpected traﬃc increases. When your application is optimally scaled out for a normal traﬃc ﬂow, the actual metric value should be at or just below the target value. When a scaling policy is based on throughput, such as the request count per target for an Application Load Balancer, network I/O, or other count metrics, the target value represents the optimal average throughput from a single entity (such as a single target of your Application Load Balancer target group), for a one-minute period. Deﬁne target value 65 Application Auto Scaling User Guide Deﬁne cooldown periods You can optionally deﬁne cooldown periods in your target tracking scaling policy. A cooldown period speciﬁes the amount of time the scaling policy waits for a previous scaling activity to take eﬀect. There are two types of cooldown periods: • With the scale-out cooldown period, the intention is to continuously (but not excessively) scale out. After Application Auto Scaling successfully scales out using a scaling policy, it starts to calculate the cooldown time. A scaling policy won‘t increase the desired capacity again unless either a larger scale out is triggered or the cooldown period ends. While the scale-out cooldown period is in eﬀect, the capacity added by the initiating scale-out activity is calculated as part of the desired capacity for the next scale-out activity. • With the scale-in cooldown period, the intention is to scale in conservatively to protect your application‘s availability, so scale-in activities are blocked until the scale-in cooldown period has expired. However, if another alarm triggers a scale-out activity during the scale-in cooldown period, Application Auto Scaling scales out the target immediately. In this case, the scale-in cooldown period stops and doesn‘t complete. Each cooldown period is measured in seconds and applies only to scaling policy-related scaling activities. During a cooldown period, when a scheduled action starts at the scheduled time, it can trigger a scaling activity immediately without waiting for the cooldown
app-as-ug-023	app-as-ug.pdf	23	the scale-in cooldown period, the intention is to scale in conservatively to protect your application‘s availability, so scale-in activities are blocked until the scale-in cooldown period has expired. However, if another alarm triggers a scale-out activity during the scale-in cooldown period, Application Auto Scaling scales out the target immediately. In this case, the scale-in cooldown period stops and doesn‘t complete. Each cooldown period is measured in seconds and applies only to scaling policy-related scaling activities. During a cooldown period, when a scheduled action starts at the scheduled time, it can trigger a scaling activity immediately without waiting for the cooldown period to expire. You can start with the default values, which can be later ﬁne-tuned. For example, you might need to increase a cooldown period to prevent your target tracking scaling policy from being too aggressive about changes that occur over short periods of time. Default values Application Auto Scaling provides a default value of 600 for ElastiCache and a default value of 300 for the following scalable targets: • AppStream 2.0 ﬂeets • Aurora DB clusters • ECS services • Neptune clusters Deﬁne cooldown periods 66 Application Auto Scaling User Guide • SageMaker AI endpoint variants • SageMaker AI inference components • SageMaker AI Serverless provisioned concurrency • Spot Fleets • Pool of WorkSpaces • Custom resources For all other scalable targets, the default value is 0 or null: • Amazon Comprehend document classiﬁcation and entity recognizer endpoints • DynamoDB tables and global secondary indexes • Amazon Keyspaces tables • Lambda provisioned concurrency • Amazon MSK broker storage Null values are treated the same as zero values when Application Auto Scaling evaluates the cooldown period. You can update any of the default values, including null values, to set your own cooldown periods. Considerations The following considerations apply when working with target tracking scaling policies: • Do not create, edit, or delete the CloudWatch alarms that are used with a target tracking scaling policy. Application Auto Scaling creates and manages the CloudWatch alarms that are associated with your target tracking scaling policies and deletes them when no longer needed. • If the metric is missing data points, this causes the CloudWatch alarm state to change to INSUFFICIENT_DATA. When this happens, Application Auto Scaling cannot scale your scalable target until new data points are found. For more information, see Conﬁguring how CloudWatch alarms treat missing data in the Amazon CloudWatch User Guide. • If the metric is sparsely reported by design, metric math can be helpful. For example, to use the most recent values, then use the FILL(m1,REPEAT) function where m1 is the metric. • You may see gaps between the target value and the actual metric data points. This is because Application Auto Scaling always acts conservatively by rounding up or down when it determines Considerations 67 Application Auto Scaling User Guide how much capacity to add or remove. This prevents it from adding insuﬃcient capacity or removing too much capacity. However, for a scalable target with a small capacity, the actual metric data points might seem far from the target value. For a scalable target with a larger capacity, adding or removing capacity causes less of a gap between the target value and the actual metric data points. • A target tracking scaling policy assumes that it should perform scale out when the speciﬁed metric is above the target value. You cannot use a target tracking scaling policy to scale out when the speciﬁed metric is below the target value. Multiple scaling policies You can have multiple target tracking scaling policies for a scalable target, provided that each of them uses a diﬀerent metric. The intention of Application Auto Scaling is to always prioritize availability, so its behavior diﬀers depending on whether the target tracking policies are ready for scale out or scale in. It will scale out the scalable target if any of the target tracking policies are ready for scale out, but will scale in only if all of the target tracking policies (with the scale-in portion enabled) are ready to scale in. If multiple scaling policies instruct the scalable target to scale out or in at the same time, Application Auto Scaling scales based on the policy that provides the largest capacity for both scale in and scale out. This provides greater ﬂexibility to cover multiple scenarios and ensures that there is always enough capacity to process your workloads. You can disable the scale-in portion of a target tracking scaling policy to use a diﬀerent method for scale in than you use for scale out. For example, you can use a step scaling policy for scale in while using a target tracking scaling policy for scale out. We recommend caution, however, when using target tracking scaling policies with step scaling policies because conﬂicts between these policies
app-as-ug-024	app-as-ug.pdf	24	policy that provides the largest capacity for both scale in and scale out. This provides greater ﬂexibility to cover multiple scenarios and ensures that there is always enough capacity to process your workloads. You can disable the scale-in portion of a target tracking scaling policy to use a diﬀerent method for scale in than you use for scale out. For example, you can use a step scaling policy for scale in while using a target tracking scaling policy for scale out. We recommend caution, however, when using target tracking scaling policies with step scaling policies because conﬂicts between these policies can cause undesirable behavior. For example, if the step scaling policy initiates a scale-in activity before the target tracking policy is ready to scale in, the scale-in activity will not be blocked. After the scale-in activity completes, the target tracking policy could instruct the scalable target to scale out again. For workloads that are cyclical in nature, you also have the option to automate capacity changes on a schedule using scheduled scaling. For each scheduled action, a new minimum capacity value and a new maximum capacity value can be deﬁned. These values form the boundaries of the Multiple scaling policies 68 Application Auto Scaling User Guide scaling policy. The combination of scheduled scaling and target tracking scaling can help reduce the impact of a sharp increase in utilization levels, when capacity is needed immediately. Commonly used commands for scaling policy creation, management, and deletion The commonly used commands for working with scaling policies include: • register-scalable-target to register AWS or custom resources as scalable targets (a resource that Application Auto Scaling can scale), and to suspend and resume scaling. • put-scaling-policy to add or modify scaling policies for an existing scalable target. • describe-scaling-activities to return information about scaling activities in an AWS Region. • describe-scaling-policies to return information about scaling policies in an AWS Region. • delete-scaling-policy to delete a scaling-policy. Related resources For information about creating target tracking scaling policies for Auto Scaling groups, see Target tracking scaling policies for Amazon EC2 Auto Scaling in the Amazon EC2 Auto Scaling User Guide. Limitations The following are limitations when using target tracking scaling policies: • The scalable target can't be an Amazon EMR cluster. Target tracking scaling policies are not supported for Amazon EMR. • When an Amazon MSK cluster is the scalable target, scale in is disabled and cannot be enabled. • You cannot use the RegisterScalableTarget or PutScalingPolicy API operations to update an AWS Auto Scaling scaling plan. • Console access to view, add, update, or remove target tracking scaling policies on scalable resources depends on the resource that you use. For more information, see AWS services that you can use with Application Auto Scaling. Commonly used commands 69 Application Auto Scaling User Guide Create a target tracking scaling policy for Application Auto Scaling using the AWS CLI This example uses AWS CLI commands to create a target racking policy for an Amazon EC2 Spot Fleet. For a diﬀerent scalable target, specify its namespace in --service-namespace, its scalable dimension in --scalable-dimension, and its resource ID in --resource-id. When using the AWS CLI, remember that your commands run in the AWS Region conﬁgured for your proﬁle. If you want to run the commands in a diﬀerent Region, either change the default Region for your proﬁle, or use the --region parameter with the command. Tasks • Step 1: Register a scalable target • Step 2: Create a target tracking scaling policy • Step 3: Describe target tracking scaling policies Step 1: Register a scalable target If you haven't already done so, register the scalable target. Use the register-scalable-target command to register a speciﬁc resource in the target service as a scalable target. The following example registers a Spot Fleet request with Application Auto Scaling. Application Auto Scaling can scale the number of instances in the Spot Fleet at a minimum of 2 instances and a maximum of 10. Replace each user input placeholder with your own information. Linux, macOS, or Unix aws application-autoscaling register-scalable-target --service-namespace ec2 \ --scalable-dimension ec2:spot-fleet-request:TargetCapacity \ --resource-id spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE \ --min-capacity 2 --max-capacity 10 Windows aws application-autoscaling register-scalable-target --service-namespace ec2 ^ --scalable-dimension ec2:spot-fleet-request:TargetCapacity ^ --resource-id spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE ^ --min-capacity 2 --max-capacity 10 Create a target tracking scaling policy 70 Application Auto Scaling Output User Guide If successful, this command returns the ARN of the scalable target. The following is example output. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Step 2: Create a target tracking scaling policy To create a target tracking scaling policy, you can use the following examples to help you get started. To create a target tracking scaling policy 1. Use the following cat command to store a target value for your scaling policy and a predeﬁned metric speciﬁcation in a JSON ﬁle named config.json in your
app-as-ug-025	app-as-ug.pdf	25	^ --min-capacity 2 --max-capacity 10 Create a target tracking scaling policy 70 Application Auto Scaling Output User Guide If successful, this command returns the ARN of the scalable target. The following is example output. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Step 2: Create a target tracking scaling policy To create a target tracking scaling policy, you can use the following examples to help you get started. To create a target tracking scaling policy 1. Use the following cat command to store a target value for your scaling policy and a predeﬁned metric speciﬁcation in a JSON ﬁle named config.json in your home directory. The following is an example target tracking conﬁguration that keeps the average CPU utilization at 50 percent. $ cat ~/config.json { "TargetValue": 50.0, "PredefinedMetricSpecification": { "PredefinedMetricType": "EC2SpotFleetRequestAverageCPUUtilization" } } For more information, see PredeﬁnedMetricSpeciﬁcation in the Application Auto Scaling API Reference. Alternatively, you can use a custom metric for scaling by creating a customized metric speciﬁcation and adding values for each parameter from CloudWatch. The following is an example target tracking conﬁguration that keeps the average utilization of the speciﬁed metric at 100. $ cat ~/config.json Step 2: Create a target tracking scaling policy 71 User Guide Application Auto Scaling { "TargetValue": 100.0, "CustomizedMetricSpecification":{ "MetricName": "MyUtilizationMetric", "Namespace": "MyNamespace", "Dimensions": [ { "Name": "MyOptionalMetricDimensionName", "Value": "MyOptionalMetricDimensionValue" } ], "Statistic": "Average", "Unit": "Percent" } } For more information, see CustomizedMetricSpeciﬁcation in the Application Auto Scaling API Reference. 2. Use the following put-scaling-policy command, along with the config.json ﬁle you created, to create a scaling policy named cpu50-target-tracking-scaling-policy. Linux, macOS, or Unix aws application-autoscaling put-scaling-policy --service-namespace ec2 \ --scalable-dimension ec2:spot-fleet-request:TargetCapacity \ --resource-id spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE \ --policy-name cpu50-target-tracking-scaling-policy --policy-type TargetTrackingScaling \ --target-tracking-scaling-policy-configuration file://config.json Windows aws application-autoscaling put-scaling-policy --service-namespace ec2 ^ --scalable-dimension ec2:spot-fleet-request:TargetCapacity ^ --resource-id spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE ^ --policy-name cpu50-target-tracking-scaling-policy --policy-type TargetTrackingScaling ^ --target-tracking-scaling-policy-configuration file://config.json Output Step 2: Create a target tracking scaling policy 72 Application Auto Scaling User Guide If successful, this command returns the ARNs and names of the two CloudWatch alarms created on your behalf. The following is example output. { "PolicyARN": "arn:aws:autoscaling:region:account- id:scalingPolicy:policy-id:resource/ec2/spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE:policyName/cpu50-target-tracking-scaling-policy", "Alarms": [ { "AlarmARN": "arn:aws:cloudwatch:region:account-id:alarm:TargetTracking- spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE-AlarmHigh-d4f0770c- b46e-434a-a60f-3b36d653feca", "AlarmName": "TargetTracking-spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE-AlarmHigh-d4f0770c-b46e-434a-a60f-3b36d653feca" }, { "AlarmARN": "arn:aws:cloudwatch:region:account-id:alarm:TargetTracking- spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE-AlarmLow-1b437334- d19b-4a63-a812-6c67aaf2910d", "AlarmName": "TargetTracking-spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE-AlarmLow-1b437334-d19b-4a63-a812-6c67aaf2910d" } ] } Step 3: Describe target tracking scaling policies You can describe all scaling policies for the speciﬁed service namespace using the following describe-scaling-policies command. aws application-autoscaling describe-scaling-policies --service-namespace ec2 You can ﬁlter the results to just the target tracking scaling policies using the --query parameter. For more information about the syntax for query, see Controlling command output from the AWS CLI in the AWS Command Line Interface User Guide. Linux, macOS, or Unix aws application-autoscaling describe-scaling-policies --service-namespace ec2 \ Step 3: Describe target tracking scaling policies 73 Application Auto Scaling User Guide --query 'ScalingPolicies[?PolicyType==`TargetTrackingScaling`]' Windows aws application-autoscaling describe-scaling-policies --service-namespace ec2 ^ --query "ScalingPolicies[?PolicyType==`TargetTrackingScaling`]" Output The following is example output. [ { "PolicyARN": "PolicyARN", "TargetTrackingScalingPolicyConfiguration": { "PredefinedMetricSpecification": { "PredefinedMetricType": "EC2SpotFleetRequestAverageCPUUtilization" }, "TargetValue": 50.0 }, "PolicyName": "cpu50-target-tracking-scaling-policy", "ScalableDimension": "ec2:spot-fleet-request:TargetCapacity", "ServiceNamespace": "ec2", "PolicyType": "TargetTrackingScaling", "ResourceId": "spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE", "Alarms": [ { "AlarmARN": "arn:aws:cloudwatch:region:account-id:alarm:TargetTracking- spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE-AlarmHigh-d4f0770c- b46e-434a-a60f-3b36d653feca", "AlarmName": "TargetTracking-spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE-AlarmHigh-d4f0770c-b46e-434a-a60f-3b36d653feca" }, { "AlarmARN": "arn:aws:cloudwatch:region:account-id:alarm:TargetTracking- spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE-AlarmLow-1b437334- d19b-4a63-a812-6c67aaf2910d", "AlarmName": "TargetTracking-spot-fleet-request/sfr-73fbd2ce- aa30-494c-8788-1cee4EXAMPLE-AlarmLow-1b437334-d19b-4a63-a812-6c67aaf2910d" } ], "CreationTime": 1515021724.807 } Step 3: Describe target tracking scaling policies 74 Application Auto Scaling ] User Guide Delete a target tracking scaling policy for Application Auto Scaling using the AWS CLI When you are ﬁnished with a target tracking scaling policy, you can delete it using the delete- scaling-policy command. The following command deletes the speciﬁed target tracking scaling policy for the speciﬁed Spot Fleet request. It also deletes the CloudWatch alarms that Application Auto Scaling created on your behalf. Linux, macOS, or Unix aws application-autoscaling delete-scaling-policy --service-namespace ec2 \ --scalable-dimension ec2:spot-fleet-request:TargetCapacity \ --resource-id spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE \ --policy-name cpu50-target-tracking-scaling-policy Windows aws application-autoscaling delete-scaling-policy --service-namespace ec2 ^ --scalable-dimension ec2:spot-fleet-request:TargetCapacity ^ --resource-id spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE ^ --policy-name cpu50-target-tracking-scaling-policy Create a target tracking scaling policy for Application Auto Scaling using metric math Using metric math, you can query multiple CloudWatch metrics and use math expressions to create new time series based on these metrics. You can visualize the resulting time series in the CloudWatch console and add them to dashboards. For more information about metric math, see Using metric math in the Amazon CloudWatch User Guide. The following considerations apply to metric math expressions: • You can query any available CloudWatch metric. Each metric is a unique combination of metric name, namespace, and zero or more dimensions. Delete a target tracking scaling policy 75 Application Auto Scaling User Guide • You can use any arithmetic operator (+ - * / ^), statistical function (such as AVG or SUM), or other function that CloudWatch supports. • You can use both metrics and the results of other math expressions in the formulas of the math expression. • Any expressions used in a metric speciﬁcation must eventually return a single time
app-as-ug-026	app-as-ug.pdf	26	considerations apply to metric math expressions: • You can query any available CloudWatch metric. Each metric is a unique combination of metric name, namespace, and zero or more dimensions. Delete a target tracking scaling policy 75 Application Auto Scaling User Guide • You can use any arithmetic operator (+ - * / ^), statistical function (such as AVG or SUM), or other function that CloudWatch supports. • You can use both metrics and the results of other math expressions in the formulas of the math expression. • Any expressions used in a metric speciﬁcation must eventually return a single time series. • You can verify that a metric math expression is valid by using the CloudWatch console or the CloudWatch GetMetricData API. Topics • Example: Amazon SQS queue backlog per task • Limitations Example: Amazon SQS queue backlog per task To calculate the Amazon SQS queue backlog per task, take the approximate number of messages available for retrieval from the queue and divide that number by the number of Amazon ECS tasks running in the service. For more information, see Amazon Elastic Container Service (ECS) Auto Scaling using custom metrics on the AWS Compute Blog. The logic for the expression is this: sum of (number of messages in the queue)/(number of tasks that are currently in the RUNNING state) Then your CloudWatch metric information is the following. ID m1 m2 CloudWatch metric Statistic Approxima teNumberO fMessagesVisible Sum Period 1 minute RunningTaskCount Average 1 minute Your metric math ID and expression are the following. Example: Amazon SQS queue backlog per task 76 Application Auto Scaling User Guide ID e1 Expression (m1)/(m2) The following diagram illustrates the architecture for this metric: To use this metric math to create a target tracking scaling policy (AWS CLI) 1. Store the metric math expression as part of a customized metric speciﬁcation in a JSON ﬁle named config.json. Use the following example to help you get started. Replace each user input placeholder with your own information. { "CustomizedMetricSpecification": { Example: Amazon SQS queue backlog per task 77 Application Auto Scaling "Metrics": [ User Guide { "Label": "Get the queue size (the number of messages waiting to be processed)", "Id": "m1", "MetricStat": { "Metric": { "MetricName": "ApproximateNumberOfMessagesVisible", "Namespace": "AWS/SQS", "Dimensions": [ { "Name": "QueueName", "Value": "my-queue" } ] }, "Stat": "Sum" }, "ReturnData": false }, { "Label": "Get the ECS running task count (the number of currently running tasks)", "Id": "m2", "MetricStat": { "Metric": { "MetricName": "RunningTaskCount", "Namespace": "ECS/ContainerInsights", "Dimensions": [ { "Name": "ClusterName", "Value": "my-cluster" }, { "Name": "ServiceName", "Value": "my-service" } ] }, "Stat": "Average" }, "ReturnData": false }, { Example: Amazon SQS queue backlog per task 78 Application Auto Scaling User Guide "Label": "Calculate the backlog per instance", "Id": "e1", "Expression": "m1 / m2", "ReturnData": true } ] }, "TargetValue": 100 } For more information, see TargetTrackingScalingPolicyConﬁguration in the Application Auto Scaling API Reference. Note Following are some additional resources that can help you ﬁnd metric names, namespaces, dimensions, and statistics for CloudWatch metrics: • For information about the available metrics for AWS services, see AWS services that publish CloudWatch metrics in the Amazon CloudWatch User Guide. • To get the exact metric name, namespace, and dimensions (if applicable) for a CloudWatch metric with the AWS CLI, see list-metrics. 2. To create this policy, run the put-scaling-policy command using the JSON ﬁle as input, as demonstrated in the following example. aws application-autoscaling put-scaling-policy --policy-name sqs-backlog-target- tracking-scaling-policy \ --service-namespace ecs --scalable-dimension ecs:service:DesiredCount --resource- id service/my-cluster/my-service \ --policy-type TargetTrackingScaling --target-tracking-scaling-policy- configuration file://config.json If successful, this command returns the policy's Amazon Resource Name (ARN) and the ARNs of the two CloudWatch alarms created on your behalf. { "PolicyARN": "arn:aws:autoscaling:us-west-2:012345678910:scalingPolicy: 8784a896-b2ba-47a1-b08c-27301cc499a1:resource/ecs/service/my-cluster/my- service:policyName/sqs-backlog-target-tracking-scaling-policy", Example: Amazon SQS queue backlog per task 79 Application Auto Scaling "Alarms": [ User Guide { "AlarmARN": "arn:aws:cloudwatch:us- west-2:012345678910:alarm:TargetTracking-service/my-cluster/my-service- AlarmHigh-9bc77b56-0571-4276-ba0f-d4178882e0a0", "AlarmName": "TargetTracking-service/my-cluster/my-service- AlarmHigh-9bc77b56-0571-4276-ba0f-d4178882e0a0" }, { "AlarmARN": "arn:aws:cloudwatch:us- west-2:012345678910:alarm:TargetTracking-service/my-cluster/my-service- AlarmLow-9b6ad934-6d37-438e-9e05-02836ddcbdc4", "AlarmName": "TargetTracking-service/my-cluster/my-service- AlarmLow-9b6ad934-6d37-438e-9e05-02836ddcbdc4" } ] } Note If this command throws an error, make sure that you have updated the AWS CLI locally to the latest version. Limitations • The maximum request size is 50 KB. This is the total payload size for the PutScalingPolicy API request when you use metric math in the policy deﬁnition. If you exceed this limit, Application Auto Scaling rejects the request. • The following services are not supported when using metric math with target tracking scaling policies: • Amazon Keyspaces (for Apache Cassandra) • DynamoDB • Amazon EMR • Amazon MSK • Amazon Neptune Limitations 80 Application Auto Scaling User Guide Step scaling policies for Application Auto Scaling A step scaling policy scales your application's capacity in predeﬁned increments based on CloudWatch alarms. You can deﬁne separate scaling policies to handle scaling out (increasing capacity) and scaling in (decreasing capacity) when an alarm threshold is breached. With step scaling policies, you
app-as-ug-027	app-as-ug.pdf	27	you exceed this limit, Application Auto Scaling rejects the request. • The following services are not supported when using metric math with target tracking scaling policies: • Amazon Keyspaces (for Apache Cassandra) • DynamoDB • Amazon EMR • Amazon MSK • Amazon Neptune Limitations 80 Application Auto Scaling User Guide Step scaling policies for Application Auto Scaling A step scaling policy scales your application's capacity in predeﬁned increments based on CloudWatch alarms. You can deﬁne separate scaling policies to handle scaling out (increasing capacity) and scaling in (decreasing capacity) when an alarm threshold is breached. With step scaling policies, you create and manage the CloudWatch alarms that invoke the scaling process. When an alarm is breached, Application Auto Scaling initiates the scaling policy associated with that alarm. The step scaling policy scales capacity using a set of adjustments, known as step adjustments. The size of the adjustment varies based on the magnitude of the alarm breach. • If the breach exceeds the ﬁrst threshold, Application Auto Scaling will apply the ﬁrst step adjustment. • If the breach exceeds the second threshold, Application Auto Scaling will apply the second step adjustment, and so on. This allows the scaling policy to respond appropriately to both minor and major changes in the alarm metric. The policy will continue to respond to additional alarm breaches, even while a scaling activity is in progress. This means Application Auto Scaling will evaluate all alarm breaches as they occur. A cooldown period is used to protect against over-scaling due to multiple alarm breaches occurring in rapid succession. Like target tracking, step scaling can help automatically scale your application's capacity as traﬃc changes occur. However, target tracking policies tend to be easier to implement and manage for steady scaling needs. Supported scalable targets You can use step scaling policies with the following scalable targets: • AppStream 2.0 ﬂeets • Aurora DB clusters • ECS services 81 User Guide Application Auto Scaling • EMR clusters • SageMaker AI endpoint variants • SageMaker AI inference components • SageMaker AI Serverless provisioned concurrency • Spot Fleets • Custom resources Contents • How step scaling for Application Auto Scaling works • Create a step scaling policy for Application Auto Scaling using the AWS CLI • Describe step scaling policies for Application Auto Scaling using the AWS CLI • Delete a step scaling policy for Application Auto Scaling using the AWS CLI How step scaling for Application Auto Scaling works This topic describes how step scaling works and introduces the key elements of a step scaling policy. Contents • How it works • Step adjustments • Scaling adjustment types • Cooldown period • Commonly used commands for scaling policy creation, management, and deletion • Considerations • Related resources • Console access How it works To use step scaling, you create a CloudWatch alarm that monitors a metric for your scalable target. Deﬁne the metric, threshold value, and number of evaluation periods that determine an alarm How step scaling works 82 Application Auto Scaling User Guide breach. You also create a step scaling policy that deﬁnes how to scale capacity when the alarm threshold is breached and associate it with your scalable target. Add the step adjustments in the policy. You can deﬁne diﬀerent step adjustments based on the breach size of the alarm. For example: • Scale out by 10 capacity units if the alarm metric reaches 60 percent • Scale out by 30 capacity units if the alarm metric reaches 75 percent • Scale out by 40 capacity units if the alarm metric reaches 85 percent When the alarm threshold is breached for the speciﬁed number of evaluation periods, Application Auto Scaling will apply the step adjustments deﬁned in the policy. The adjustments can continue for additional alarm breaches until the alarm state returns to OK. Scaling activities are performed with cooldown periods between them to prevent rapid ﬂuctuations in capacity. You can optionally conﬁgure the cooldown periods for your scaling policy. Step adjustments When you create a step scaling policy, you specify one or more step adjustments that automatically scale the capacity of the target dynamically based on the size of the alarm breach. Each step adjustment speciﬁes the following: • A lower bound for the metric value • An upper bound for the metric value • The amount by which to scale, based on the scaling adjustment type CloudWatch aggregates metric data points based on the statistic for the metric associated with your CloudWatch alarm. When the alarm is breached, the appropriate scaling policy is invoked. Application Auto Scaling applies your speciﬁed aggregation type to the most recent metric data points from CloudWatch (as opposed to the raw metric data). It compares this aggregated metric value against the upper and lower bounds deﬁned by the step adjustments to determine which
app-as-ug-028	app-as-ug.pdf	28	A lower bound for the metric value • An upper bound for the metric value • The amount by which to scale, based on the scaling adjustment type CloudWatch aggregates metric data points based on the statistic for the metric associated with your CloudWatch alarm. When the alarm is breached, the appropriate scaling policy is invoked. Application Auto Scaling applies your speciﬁed aggregation type to the most recent metric data points from CloudWatch (as opposed to the raw metric data). It compares this aggregated metric value against the upper and lower bounds deﬁned by the step adjustments to determine which step adjustment to perform. You specify the upper and lower bounds relative to the breach threshold. For example, let's say you made a CloudWatch alarm and a scale-out policy for when the metric is above 50 percent. You then made a second alarm and a scale-in policy for when the metric is below 50 percent. You made a set of step adjustments with an adjustment type of PercentChangeInCapacity for each policy: Step adjustments 83 Application Auto Scaling User Guide Example: Step adjustments for scale-out policy Lower bound Upper bound Adjustment 0 10 20 10 20 null 0 10 30 Example: Step adjustments for scale-in policy Lower bound Upper bound Adjustment -10 -20 null 0 -10 -20 0 -10 -30 This creates the following scaling conﬁguration. Metric value -infinity 30% 40% 60% 70% infinity ----------------------------------------------------------------------- -30% \| -10% \| Unchanged \| +10% \| +30% ----------------------------------------------------------------------- Now, let's say that you use this scaling conﬁguration on a scalable target that has a capacity of 10. The following points summarize the behavior of the scaling conﬁguration in relation to the capacity of the scalable target: • The original capacity is maintained while the aggregated metric value is greater than 40 and less than 60. • If the metric value gets to 60, Application Auto Scaling increases the capacity of the scalable target by 1, to 11. That's based on the second step adjustment of the scale-out policy (add 10 percent of 10). After the new capacity is added, Application Auto Scaling increases the current capacity to 11. If the metric value rises to 70 even after this increase in capacity, Application Auto Step adjustments 84 Application Auto Scaling User Guide Scaling increases the target capacity by 3, to 14. That's based on the third step adjustment of the scale-out policy (add 30 percent of 11, 3.3, rounded down to 3). • If the metric value gets to 40, Application Auto Scaling decreases the capacity of the scalable target by 1, to 13, based on the second step adjustment of the scale-in policy (remove 10 percent of 14, 1.4, rounded down to 1). If the metric value falls to 30 even after this decrease in capacity, Application Auto Scaling decreases the target capacity by 3, to 10, based on the third step adjustment of the scale-in policy (remove 30 percent of 13, 3.9, rounded down to 3). When you specify the step adjustments for your scaling policy, note the following: • The ranges of your step adjustments can't overlap or have a gap. • Only one step adjustment can have a null lower bound (negative inﬁnity). If one step adjustment has a negative lower bound, then there must be a step adjustment with a null lower bound. • Only one step adjustment can have a null upper bound (positive inﬁnity). If one step adjustment has a positive upper bound, then there must be a step adjustment with a null upper bound. • The upper and lower bound can't be null in the same step adjustment. • If the metric value is above the breach threshold, the lower bound is inclusive and the upper bound is exclusive. If the metric value is below the breach threshold, the lower bound is exclusive and the upper bound is inclusive. Scaling adjustment types You can deﬁne a scaling policy that performs the optimal scaling action, based on the scaling adjustment type that you choose. You can specify the adjustment type as a percentage of the current capacity of your scalable target or in absolute numbers. Application Auto Scaling supports the following adjustment types for step scaling policies: • ChangeInCapacity—Increase or decrease the current capacity of the scalable target by the speciﬁed value. A positive value increases the capacity and a negative value decreases the capacity. For example: If the current capacity is 3 and the adjustment is 5, then Application Auto Scaling adds 5 to the capacity for a total of 8. • ExactCapacity—Change the current capacity of the scalable target to the speciﬁed value. Specify a non-negative value with this adjustment type. For example: If the current capacity is 3 and the adjustment is 5, then Application Auto Scaling changes the capacity to 5. Scaling adjustment types
app-as-ug-029	app-as-ug.pdf	29	ChangeInCapacity—Increase or decrease the current capacity of the scalable target by the speciﬁed value. A positive value increases the capacity and a negative value decreases the capacity. For example: If the current capacity is 3 and the adjustment is 5, then Application Auto Scaling adds 5 to the capacity for a total of 8. • ExactCapacity—Change the current capacity of the scalable target to the speciﬁed value. Specify a non-negative value with this adjustment type. For example: If the current capacity is 3 and the adjustment is 5, then Application Auto Scaling changes the capacity to 5. Scaling adjustment types 85 Application Auto Scaling User Guide • PercentChangeInCapacity—Increase or decrease the current capacity of the scalable target by the speciﬁed percentage. A positive value increases the capacity and a negative value decreases the capacity. For example: If the current capacity is 10 and the adjustment is 10 percent, then Application Auto Scaling adds 1 to the capacity for a total of 11. If the resulting value is not an integer, Application Auto Scaling rounds it as follows: • Values greater than 1 are rounded down. For example, 12.7 is rounded to 12. • Values between 0 and 1 are rounded to 1. For example, .67 is rounded to 1. • Values between 0 and -1 are rounded to -1. For example, -.58 is rounded to -1. • Values less than -1 are rounded up. For example, -6.67 is rounded to -6. With PercentChangeInCapacity, you can also specify the minimum amount to scale using the MinAdjustmentMagnitude parameter. For example, suppose that you create a policy that adds 25 percent and you specify a minimum amount of 2. If the scalable target has a capacity of 4 and the scaling policy is performed, 25 percent of 4 is 1. However, because you speciﬁed a minimum increment of 2, Application Auto Scaling adds 2. Cooldown period You can optionally deﬁne a cooldown period in your step scaling policy. A cooldown period speciﬁes the amount of time the scaling policy waits for a previous scaling activity to take eﬀect. There are two ways to plan for the use of cooldown periods for a step scaling conﬁguration: • With the cooldown period for scale-out policies, the intention is to continuously (but not excessively) scale out. After Application Auto Scaling successfully scales out using a scaling policy, it starts to calculate the cooldown time. A scaling policy won‘t increase the desired capacity again unless either a larger scale out is triggered or the cooldown period ends. While the scale-out cooldown period is in eﬀect, the capacity added by the initiating scale-out activity is calculated as part of the desired capacity for the next scale-out activity. • With the cooldown period for scale-in policies, the intention is to scale in conservatively to protect your application‘s availability, so scale-in activities are blocked until the scale-in cooldown period has expired. However, if another alarm triggers a scale-out activity during the scale-in cooldown period, Application Auto Scaling scales out the target immediately. In this case, the scale-in cooldown period stops and doesn‘t complete. Cooldown period 86 Application Auto Scaling User Guide For example, when a traﬃc peak occurs, an alarm is triggered and Application Auto Scaling automatically adds capacity to help handle the increased load. If you set a cooldown period for your scale-out policy, when the alarm triggers the policy to increase the capacity by 2, the scaling activity completes successfully, and the scale-out cooldown period starts. If an alarm triggers again during the cooldown period but at a more aggressive step adjustment of 3, the previous increase of 2 is considered part of the current capacity. Therefore, only 1 is added to the capacity. This allows faster scaling than waiting for the cooldown to expire but without adding more capacity than you need. The cooldown period is measured in seconds and applies only to scaling policy-related scaling activities. During a cooldown period, when a scheduled action starts at the scheduled time, it can trigger a scaling activity immediately without waiting for the cooldown period to expire. The default value is 300 if no value is speciﬁed. Commonly used commands for scaling policy creation, management, and deletion The commonly used commands for working with scaling policies include: • register-scalable-target to register AWS or custom resources as scalable targets (a resource that Application Auto Scaling can scale), and to suspend and resume scaling. • put-scaling-policy to add or modify scaling policies for an existing scalable target. • describe-scaling-activities to return information about scaling activities in an AWS Region. • describe-scaling-policies to return information about scaling policies in an AWS Region. • delete-scaling-policy to delete a scaling-policy. Considerations The following considerations apply when working with step scaling policies: • Consider whether you can predict the step adjustments on the application accurately
app-as-ug-030	app-as-ug.pdf	30	used commands for working with scaling policies include: • register-scalable-target to register AWS or custom resources as scalable targets (a resource that Application Auto Scaling can scale), and to suspend and resume scaling. • put-scaling-policy to add or modify scaling policies for an existing scalable target. • describe-scaling-activities to return information about scaling activities in an AWS Region. • describe-scaling-policies to return information about scaling policies in an AWS Region. • delete-scaling-policy to delete a scaling-policy. Considerations The following considerations apply when working with step scaling policies: • Consider whether you can predict the step adjustments on the application accurately enough to use step scaling. If your scaling metric increases or decreases proportionally to the capacity of the scalable target, we recommend that you use a target tracking scaling policy instead. You still have the option to use step scaling as an additional policy for a more advanced conﬁguration. For example, you can conﬁgure a more aggressive response when utilization reaches a certain level. Commonly used commands 87 Application Auto Scaling User Guide • Make sure to choose an adequate margin between the scale-out and scale-in thresholds to prevent ﬂapping. Flapping is an inﬁnite loop of scaling in and scaling out. That is, if a scaling action is taken, the metric value would change and start another scaling action in the reverse direction. Related resources For information about creating step scaling policies for Auto Scaling groups, see Step and simple scaling policies for Amazon EC2 Auto Scaling in the Amazon EC2 Auto Scaling User Guide. Console access Console access to view, add, update, or remove step scaling policies on scalable resources depends on the resource that you use. For more information, see AWS services that you can use with Application Auto Scaling. Create a step scaling policy for Application Auto Scaling using the AWS CLI This example uses AWS CLI commands to create a step scaling policy for an Amazon ECS service. For a diﬀerent scalable target, specify its namespace in --service-namespace, its scalable dimension in --scalable-dimension, and its resource ID in --resource-id. When using the AWS CLI, remember that your commands run in the AWS Region conﬁgured for your proﬁle. If you want to run the commands in a diﬀerent Region, either change the default Region for your proﬁle, or use the --region parameter with the command. Tasks • Step 1: Register a scalable target • Step 2: Create a step scaling policy • Step 3: Create an alarm that invokes a scaling policy Step 1: Register a scalable target If you haven't already done so, register the scalable target. Use the register-scalable-target command to register a speciﬁc resource in the target service as a scalable target. The following Related resources 88 Application Auto Scaling User Guide example registers an Amazon ECS service with Application Auto Scaling. Application Auto Scaling can scale the number of tasks at a minimum of 2 tasks and a maximum of 10. Replace each user input placeholder with your own information. Linux, macOS, or Unix aws application-autoscaling register-scalable-target --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id service/my-cluster/my-service \ --min-capacity 2 --max-capacity 10 Windows aws application-autoscaling register-scalable-target --service-namespace ecs ^ --scalable-dimension ecs:service:DesiredCount ^ --resource-id service/my-cluster/my-service ^ --min-capacity 2 --max-capacity 10 Output If successful, this command returns the ARN of the scalable target. The following is example output. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Step 2: Create a step scaling policy To create a step scaling policy for your scalable target, you can use the following examples to help you get started. Scale out To create a step scaling policy for scale out (increase capacity) 1. Use the following cat command to store a step scaling policy conﬁguration in a JSON ﬁle named config.json in your home directory. The following is an example conﬁguration with an adjustment type of PercentChangeInCapacity that increases the capacity of Step 2: Create a step scaling policy 89 Application Auto Scaling User Guide the scalable target based on the following step adjustments (assuming a CloudWatch alarm threshold of 70): • Increase capacity by 10 percent when the value of the metric is greater than or equal to 70 but less than 85 • Increase capacity by 20 percent when the value of the metric is greater than or equal to 85 but less than 95 • Increase capacity by 30 percent when the value of the metric is greater than or equal to 95 $ cat ~/config.json { "AdjustmentType": "PercentChangeInCapacity", "MetricAggregationType": "Average", "Cooldown": 60, "MinAdjustmentMagnitude": 1, "StepAdjustments": [ { "MetricIntervalLowerBound": 0.0, "MetricIntervalUpperBound": 15.0, "ScalingAdjustment": 10 }, { "MetricIntervalLowerBound": 15.0, "MetricIntervalUpperBound": 25.0, "ScalingAdjustment": 20 }, { "MetricIntervalLowerBound": 25.0, "ScalingAdjustment": 30 } ] } For more information, see StepScalingPolicyConﬁguration in the Application Auto Scaling API Reference. 2. Use the following put-scaling-policy command, along with the config.json ﬁle that you created, to create a scaling policy
app-as-ug-031	app-as-ug.pdf	31	metric is greater than or equal to 85 but less than 95 • Increase capacity by 30 percent when the value of the metric is greater than or equal to 95 $ cat ~/config.json { "AdjustmentType": "PercentChangeInCapacity", "MetricAggregationType": "Average", "Cooldown": 60, "MinAdjustmentMagnitude": 1, "StepAdjustments": [ { "MetricIntervalLowerBound": 0.0, "MetricIntervalUpperBound": 15.0, "ScalingAdjustment": 10 }, { "MetricIntervalLowerBound": 15.0, "MetricIntervalUpperBound": 25.0, "ScalingAdjustment": 20 }, { "MetricIntervalLowerBound": 25.0, "ScalingAdjustment": 30 } ] } For more information, see StepScalingPolicyConﬁguration in the Application Auto Scaling API Reference. 2. Use the following put-scaling-policy command, along with the config.json ﬁle that you created, to create a scaling policy named my-step-scaling-policy. Step 2: Create a step scaling policy 90 Application Auto Scaling User Guide Linux, macOS, or Unix aws application-autoscaling put-scaling-policy --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id service/my-cluster/my-service \ --policy-name my-step-scaling-policy --policy-type StepScaling \ --step-scaling-policy-configuration file://config.json Windows aws application-autoscaling put-scaling-policy --service-namespace ecs ^ --scalable-dimension ecs:service:DesiredCount ^ --resource-id service/my-cluster/my-service ^ --policy-name my-step-scaling-policy --policy-type StepScaling ^ --step-scaling-policy-configuration file://config.json Output The output includes the ARN that serves as a unique name for the policy. You need it to create a CloudWatch alarm for your policy. The following is example output. { "PolicyARN": "arn:aws:autoscaling:region:123456789012:scalingPolicy:ac542982-cbeb-4294-891c- a5a941dfa787:resource/ecs/service/my-cluster/my-service:policyName/my-step- scaling-policy" } Scale in To create a step scaling policy for scale in (decrease capacity) 1. Use the following cat command to store a step scaling policy conﬁguration in a JSON ﬁle named config.json in your home directory. The following is an example conﬁguration with an adjustment type of ChangeInCapacity that decreases the capacity of the scalable target based on the following step adjustments (assuming a CloudWatch alarm threshold of 50): Step 2: Create a step scaling policy 91 Application Auto Scaling User Guide • Decrease capacity by 1 when the value of the metric is less than or equal to 50 but greater than 40 • Decrease capacity by 2 when the value of the metric is less than or equal to 40 but greater than 30 • Decrease capacity by 3 when the value of the metric is less than or equal to 30 $ cat ~/config.json { "AdjustmentType": "ChangeInCapacity", "MetricAggregationType": "Average", "Cooldown": 60, "StepAdjustments": [ { "MetricIntervalUpperBound": 0.0, "MetricIntervalLowerBound": -10.0, "ScalingAdjustment": -1 }, { "MetricIntervalUpperBound": -10.0, "MetricIntervalLowerBound": -20.0, "ScalingAdjustment": -2 }, { "MetricIntervalUpperBound": -20.0, "ScalingAdjustment": -3 } ] } For more information, see StepScalingPolicyConﬁguration in the Application Auto Scaling API Reference. 2. Use the following put-scaling-policy command, along with the config.json ﬁle that you created, to create a scaling policy named my-step-scaling-policy. Linux, macOS, or Unix aws application-autoscaling put-scaling-policy --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id service/my-cluster/my-service \ Step 2: Create a step scaling policy 92 Application Auto Scaling User Guide --policy-name my-step-scaling-policy --policy-type StepScaling \ --step-scaling-policy-configuration file://config.json Windows aws application-autoscaling put-scaling-policy --service-namespace ecs ^ --scalable-dimension ecs:service:DesiredCount ^ --resource-id service/my-cluster/my-service ^ --policy-name my-step-scaling-policy --policy-type StepScaling ^ --step-scaling-policy-configuration file://config.json Output The output includes the ARN that serves as a unique name for the policy. You need this ARN to create a CloudWatch alarm for your policy. The following is example output. { "PolicyARN": "arn:aws:autoscaling:region:123456789012:scalingPolicy:ac542982-cbeb-4294-891c- a5a941dfa787:resource/ecs/service/my-cluster/my-service:policyName/my-step- scaling-policy" } Step 3: Create an alarm that invokes a scaling policy Finally, use the following CloudWatch put-metric-alarm command to create an alarm to use with your step scaling policy. In this example, you have an alarm based on average CPU utilization. The alarm is conﬁgured to be in an ALARM state if it reaches a threshold of 70 percent for at least two consecutive evaluation periods of 60 seconds. To specify a diﬀerent CloudWatch metric or use your own custom metric, specify its name in --metric-name and its namespace in --namespace. Linux, macOS, or Unix aws cloudwatch put-metric-alarm --alarm-name Step-Scaling-AlarmHigh-ECS:service/my- cluster/my-service \ --metric-name CPUUtilization --namespace AWS/ECS --statistic Average \ --period 60 --evaluation-periods 2 --threshold 70 \ --comparison-operator GreaterThanOrEqualToThreshold \ --dimensions Name=ClusterName,Value=default Name=ServiceName,Value=sample-app-service \ Step 3: Create an alarm that invokes a scaling policy 93 Application Auto Scaling --alarm-actions PolicyARN Windows User Guide aws cloudwatch put-metric-alarm --alarm-name Step-Scaling-AlarmHigh-ECS:service/my- cluster/my-service ^ --metric-name CPUUtilization --namespace AWS/ECS --statistic Average ^ --period 60 --evaluation-periods 2 --threshold 70 ^ --comparison-operator GreaterThanOrEqualToThreshold ^ --dimensions Name=ClusterName,Value=default Name=ServiceName,Value=sample-app-service ^ --alarm-actions PolicyARN Describe step scaling policies for Application Auto Scaling using the AWS CLI You can describe all scaling policies for a service namespace using the describe-scaling-policies command. The following example describes all scaling policies for all Amazon ECS services. To list them for a speciﬁc Amazon ECS service only add the --resource-id option. aws application-autoscaling describe-scaling-policies --service-namespace ecs You can ﬁlter the results to just the step scaling policies using the --query parameter. For more information about the syntax for query, see Controlling command output from the AWS CLI in the AWS Command Line Interface User Guide. Linux, macOS, or Unix aws application-autoscaling describe-scaling-policies --service-namespace ecs \ --query 'ScalingPolicies[?PolicyType==`StepScaling`]' Windows aws application-autoscaling describe-scaling-policies --service-namespace ecs ^ --query "ScalingPolicies[?PolicyType==`StepScaling`]" Output Describe step scaling policies 94 User Guide Application Auto
app-as-ug-032	app-as-ug.pdf	32	following example describes all scaling policies for all Amazon ECS services. To list them for a speciﬁc Amazon ECS service only add the --resource-id option. aws application-autoscaling describe-scaling-policies --service-namespace ecs You can ﬁlter the results to just the step scaling policies using the --query parameter. For more information about the syntax for query, see Controlling command output from the AWS CLI in the AWS Command Line Interface User Guide. Linux, macOS, or Unix aws application-autoscaling describe-scaling-policies --service-namespace ecs \ --query 'ScalingPolicies[?PolicyType==`StepScaling`]' Windows aws application-autoscaling describe-scaling-policies --service-namespace ecs ^ --query "ScalingPolicies[?PolicyType==`StepScaling`]" Output Describe step scaling policies 94 User Guide Application Auto Scaling The following is example output. [ { "PolicyARN": "PolicyARN", "StepScalingPolicyConfiguration": { "MetricAggregationType": "Average", "Cooldown": 60, "StepAdjustments": [ { "MetricIntervalLowerBound": 0.0, "MetricIntervalUpperBound": 15.0, "ScalingAdjustment": 1 }, { "MetricIntervalLowerBound": 15.0, "MetricIntervalUpperBound": 25.0, "ScalingAdjustment": 2 }, { "MetricIntervalLowerBound": 25.0, "ScalingAdjustment": 3 } ], "AdjustmentType": "ChangeInCapacity" }, "PolicyType": "StepScaling", "ResourceId": "service/my-cluster/my-service", "ServiceNamespace": "ecs", "Alarms": [ { "AlarmName": "Step-Scaling-AlarmHigh-ECS:service/my-cluster/my- service", "AlarmARN": "arn:aws:cloudwatch:region:012345678910:alarm:Step-Scaling- AlarmHigh-ECS:service/my-cluster/my-service" } ], "PolicyName": "my-step-scaling-policy", "ScalableDimension": "ecs:service:DesiredCount", "CreationTime": 1515024099.901 } ] Describe step scaling policies 95 Application Auto Scaling User Guide Delete a step scaling policy for Application Auto Scaling using the AWS CLI When you no longer need a step scaling policy, you can delete it. To delete both the scaling policy and the associated CloudWatch alarm, complete the following tasks. To delete your scaling policy Use the delete-scaling-policy command. Linux, macOS, or Unix aws application-autoscaling delete-scaling-policy --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id service/my-cluster/my-service \ --policy-name my-step-scaling-policy Windows aws application-autoscaling delete-scaling-policy --service-namespace ecs ^ --scalable-dimension ecs:service:DesiredCount ^ --resource-id service/my-cluster/my-service ^ --policy-name my-step-scaling-policy To delete the CloudWatch alarm Use the delete-alarms command. You can delete one or more alarms at a time. For example, use the following command to delete the Step-Scaling-AlarmHigh-ECS:service/my-cluster/ my-service and Step-Scaling-AlarmLow-ECS:service/my-cluster/my-service alarms. aws cloudwatch delete-alarms --alarm-name Step-Scaling-AlarmHigh-ECS:service/my- cluster/my-service Step-Scaling-AlarmLow-ECS:service/my-cluster/my-service Delete a step scaling policy 96 Application Auto Scaling User Guide Predictive scaling for Application Auto Scaling Predictive scaling proactively scales your application. Predictive scaling analyzes historical load data to detect daily or weekly patterns in traﬃc ﬂows. It uses this information to forecast future capacity needs to proactively increase the capacity of your application to match the anticipated load. Predictive scaling is well suited for situations where you have: • Cyclical traﬃc, such as high use of resources during regular business hours and low use of resources during evenings and weekends • Recurring on-and-oﬀ workload patterns, such as batch processing, testing, or periodic data analysis. • Applications that take a long time to initialize, causing a noticeable latency impact on application performance during scale-out events Contents • How Application Auto Scaling predictive scaling works • Create a predictive scaling policy for Application Auto Scaling • Override forecast values using scheduled actions • Advanced predictive scaling policy using custom metrics How Application Auto Scaling predictive scaling works To use predictive scaling, create a predictive scaling policy that speciﬁes the CloudWatch metric to monitor and analyze. You can use a predeﬁned metric or a custom metric. For predictive scaling to start forecasting future values, this metric must have at least 24 hours of data. After you create the policy, predictive scaling starts analyzing metric data from up to the past 14 days to identify patterns. It uses this analysis to generate an hourly forecast of capacity requirements for the next 48 hours. The forecast is updated every 6 hours using the latest CloudWatch data. As new data comes in, predictive scaling is able to continuously improve the accuracy of future forecasts. How it works 97 Application Auto Scaling User Guide You can ﬁrst enable predictive scaling in forecast only mode. In this mode, it generates capacity forecasts but does not actually scale your capacity based on those forecasts. This allows you to evaluate the accuracy and suitability of the forecast. After you review the forecast data and decide to start scaling based on that data, switch the scaling policy to forecast and scale mode. In this mode: • If the forecast expects an increase in load, predictive scaling will increase the capacity. • If the forecast expects a decrease in load, predictive scaling will not scale in to remove capacity. This ensures that you scale-in only when the demand actually drops, and not just on predictions. To remove capacity that is no longer needed, you must create a Target Tracking or Step Scaling policy because they respond to real time metric data. By default, predictive scaling scales your scalable targets at the start of each hour based on the forecast for that hour. You can optionally specify an earlier start time by using the SchedulingBufferTime property in the PutScalingPolicy API operation. This allows you to launch predicted capacity ahead of the forecasted demand, which gives the new capacity adequate time to become ready to handle traﬃc. Maximum capacity limit
app-as-ug-033	app-as-ug.pdf	33	actually drops, and not just on predictions. To remove capacity that is no longer needed, you must create a Target Tracking or Step Scaling policy because they respond to real time metric data. By default, predictive scaling scales your scalable targets at the start of each hour based on the forecast for that hour. You can optionally specify an earlier start time by using the SchedulingBufferTime property in the PutScalingPolicy API operation. This allows you to launch predicted capacity ahead of the forecasted demand, which gives the new capacity adequate time to become ready to handle traﬃc. Maximum capacity limit By default, when scaling policies are set, they cannot increase capacity higher than its maximum capacity. Alternatively, you can allow the scalable target's maximum capacity to be automatically increased if the forecast capacity approaches or exceeds the maximum capacity of the scalable target. To enable this behavior, use the MaxCapacityBreachBehavior and MaxCapacityBuffer properties in the PutScalingPolicy API operation or the Max capacity behavior setting in the AWS Management Console. Warning Use caution when allowing the maximum capacity to be automatically increased. The maximum capacity does not automatically decrease back to the original maximum. Maximum capacity limit 98 Application Auto Scaling User Guide Commonly used commands for scaling policy creation, management, and deletion The commonly used commands for working with predictive scaling policies include: • register-scalable-target to register AWS or custom resources as scalable targets, to suspend scaling, and to resume scaling. • put-scaling-policy to create a predictive scaling policy. • get-predictive-scaling-forecast to retrieve the forecast data for a predictive scaling policy. • describe-scaling-activities to return information about scaling activities in an AWS Region. • describe-scaling-policies to return information about scaling policies in an AWS Region. • delete-scaling-policy to delete a scaling policy. Custom metrics Custom metrics can be used to predict the capacity needed for an application. Custom metrics are useful when predeﬁned metrics are not enough to capture the load on your application. Considerations The following considerations apply when working with predictive scaling. • Conﬁrm whether predictive scaling is suitable for your application. An application is a good ﬁt for predictive scaling if it exhibits recurring load patterns that are speciﬁc to the day of the week or the time of day. Evaluate the forecast before letting predictive scaling actively scale your application. • Predictive scaling needs at least 24 hours of historical data to start forecasting. However, forecasts are more eﬀective if historical data spans two full weeks. • Choose a load metric that accurately represents the full load on your application and is the aspect of your application that's most important to scale on. Commonly used commands for scaling policy creation, management, and deletion 99 Application Auto Scaling User Guide Create a predictive scaling policy for Application Auto Scaling The following example policy uses the AWS CLI to conﬁgure a predictive scaling policy for Amazon ECS service. Replace each user input placeholder with your own information. For more information about the CloudWatch metrics you can specify, see PredictiveScalingMetricSpeciﬁcation in the Amazon EC2 Auto Scaling API Reference. The following is an example policy with a predeﬁned memory conﬁguration. cat policy.json { "MetricSpecifications": [ { "TargetValue": 40, "PredefinedMetricPairSpecification": { "PredefinedMetricType": "ECSServiceMemoryUtilization" } } ], "SchedulingBufferTime": 3600, "MaxCapacityBreachBehavior": "HonorMaxCapacity", "Mode": "ForecastOnly" } The following example illustrates creating the policy by running the put-scaling-policy command with the conﬁguration ﬁle speciﬁed. aws aas put-scaling-policy \ --service-namespace ecs \ --region us-east-1 \ --policy-name predictive-scaling-policy-example \ --resource-id service/MyCluster/test \ --policy-type PredictiveScaling \ --scalable-dimension ecs:service:DesiredCount \ --predictive-scaling-policy-configuration file://policy.json If successful, this command returns the policy's ARN. { Create a predictive scaling policy 100 Application Auto Scaling User Guide "PolicyARN": "arn:aws:autoscaling:us- east-1:012345678912:scalingPolicy:d1d72dfe-5fd3-464f-83cf-824f16cb88b7:resource/ecs/ service/MyCluster/test:policyName/predictive-scaling-policy-example", "Alarms": [] } Override forecast values using scheduled actions Sometimes, you might have additional information about your future application requirements that the forecast calculation is unable to take into account. For example, forecast calculations might underestimate the capacity needed for an upcoming marketing event. You can use scheduled actions to temporarily override the forecast during future time periods. The scheduled actions can run on a recurring basis, or at a speciﬁc date and time when there are one-time demand ﬂuctuations. For example, you can create a scheduled action with a higher minimum capacity than what is forecasted. At runtime, Application Auto Scaling updates the minimum capacity of your scalable target. Because predictive scaling optimizes for capacity, a scheduled action with a minimum capacity that is higher than the forecast values is honored. This prevents capacity from being less than expected. To stop overriding the forecast, use a second scheduled action to return the minimum capacity to its original setting. The following procedure outlines the steps for overriding the forecast during future time periods. Topics • Step 1: (Optional) Analyze time series data • Step 2: Create two scheduled actions Important This topic assumes
app-as-ug-034	app-as-ug.pdf	34	than what is forecasted. At runtime, Application Auto Scaling updates the minimum capacity of your scalable target. Because predictive scaling optimizes for capacity, a scheduled action with a minimum capacity that is higher than the forecast values is honored. This prevents capacity from being less than expected. To stop overriding the forecast, use a second scheduled action to return the minimum capacity to its original setting. The following procedure outlines the steps for overriding the forecast during future time periods. Topics • Step 1: (Optional) Analyze time series data • Step 2: Create two scheduled actions Important This topic assumes that you are trying to override the forecast to scale to a higher capacity than what is forecasted. If you need to temporarily decrease capacity without interference from a predictive scaling policy, use forecast only mode instead. While in forecast only mode, predictive scaling will continue to generate forecasts, but it will not automatically increase capacity. You can then monitor resource utilization and manually decrease the size of your group as needed. Override the forecast 101 Application Auto Scaling User Guide Step 1: (Optional) Analyze time series data Start by analyzing the forecast time series data. This is an optional step, but it is helpful if you want to understand the details of the forecast. 1. Retrieve the forecast After the forecast is created, you can query for a speciﬁc time period in the forecast. The goal of the query is to get a complete view of the time series data for a speciﬁc time period. Your query can include up to two days of future forecast data. If you have been using predictive scaling for a while, you can also access your past forecast data. However, the maximum time duration between the start and end time is 30 days. To retrieve the forecast, use the get-predictive-scaling-forecast command. The following example gets the predictive scaling forecast for the Amazon ECS service. aws application-autoscaling get-predictive-scaling-forecast --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id 1234567890abcdef0 --policy-name predictive-scaling-policy \ --start-time "2021-05-19T17:00:00Z" \ --end-time "2021-05-19T23:00:00Z" The response includes two forecasts: LoadForecast and CapacityForecast. LoadForecast shows the hourly load forecast. CapacityForecast shows forecast values for the capacity that is needed on an hourly basis to handle the forecasted load while maintaining a speciﬁed TargetValue. 2. Identify the target time period Identify the hour or hours when the one-time demand ﬂuctuation should take place. Remember that dates and times shown in the forecast are in UTC. Step 2: Create two scheduled actions Next, create two scheduled actions for a speciﬁc time period when your application will have a higher than forecasted load. For example, if you have a marketing event that will drive traﬃc to your site for a limited period of time, you can schedule a one-time action to update the minimum Step 1: (Optional) Analyze time series data 102 Application Auto Scaling User Guide capacity when it starts. Then, schedule another action to return the minimum capacity to the original setting when the event ends. To create two scheduled actions for one-time events (AWS CLI) To create the scheduled actions, use the put-scheduled-action command. The following example deﬁnes an schedule for Amazon EC2 Auto Scaling that maintains a minimum capacity of three instances on May 19 at 5:00 PM for eight hours. The following commands show how to implement this scenario. The ﬁrst put-scheduled-update-group-action command instructs Amazon EC2 Auto Scaling to update the minimum capacity of the speciﬁed Auto Scaling group at 5:00 PM UTC on May 19, 2021. aws autoscaling put-scheduled-update-group-action --scheduled-action-name my-event- start \ --auto-scaling-group-name my-asg --start-time "2021-05-19T17:00:00Z" --minimum- capacity 3 The second command instructs Amazon EC2 Auto Scaling to set the group's minimum capacity to one at 1:00 AM UTC on May 20, 2021. aws autoscaling put-scheduled-update-group-action --scheduled-action-name my-event-end \ --auto-scaling-group-name my-asg --start-time "2021-05-20T01:00:00Z" --minimum- capacity 1 After you add these scheduled actions to the Auto Scaling group, Amazon EC2 Auto Scaling does the following: • At 5:00 PM UTC on May 19, 2021, the ﬁrst scheduled action runs. If the group currently has fewer than three instances, the group scales out to three instances. During this time and for the next eight hours, Amazon EC2 Auto Scaling can continue to scale out if the predicted capacity is higher than the actual capacity or if there is a dynamic scaling policy in eﬀect. • At 1:00 AM UTC on May 20, 2021, the second scheduled action runs. This returns the minimum capacity to its original setting at the end of the event. Step 2: Create two scheduled actions 103 Application Auto Scaling User Guide Scaling based on recurring schedules To override the forecast for the same time period every week, create two scheduled actions and provide the time and date logic using a cron expression. The cron expression format consists of ﬁve
app-as-ug-035	app-as-ug.pdf	35	out if the predicted capacity is higher than the actual capacity or if there is a dynamic scaling policy in eﬀect. • At 1:00 AM UTC on May 20, 2021, the second scheduled action runs. This returns the minimum capacity to its original setting at the end of the event. Step 2: Create two scheduled actions 103 Application Auto Scaling User Guide Scaling based on recurring schedules To override the forecast for the same time period every week, create two scheduled actions and provide the time and date logic using a cron expression. The cron expression format consists of ﬁve ﬁelds separated by spaces: [Minute] [Hour] [Day_of_Month] [Month_of_Year] [Day_of_Week]. Fields can contain any allowed values, including special characters. For example, the following cron expression runs the action every Tuesday at 6:30 AM. The asterisk is used as a wildcard to match all values for a ﬁeld. 30 6 * * 2 Advanced predictive scaling policy using custom metrics In a predictive scaling policy, you can use predeﬁned or custom metrics. Custom metrics are useful when the predeﬁned metrics do not suﬃciently describe your application load. When creating a predictive scaling policy with custom metrics, you can specify other CloudWatch metrics provided by AWS, or you can specify metrics that you deﬁne and publish yourself. You can also use metric math to aggregate and transform existing metrics into a new time series that AWS doesn't automatically track. When you combine values in your data, for example, by calculating new sums or averages, it's called aggregating. The resulting data is called an aggregate. The following section contains best practices and examples of how to construct the JSON structure for the policy. Topics • Best practices • Prerequisites • Constructing the JSON for custom metrics • Considerations for custom metrics in a predictive scaling policy Best practices The following best practices can help you use custom metrics more eﬀectively: Use custom metrics 104 Application Auto Scaling User Guide • For the load metric speciﬁcation, the most useful metric is a metric that represents the load on your application. • The scaling metric must be inversely proportional to capacity. That is, if the scalable target increases, the scaling metric should decrease by roughly the same proportion. To ensure that predictive scaling behaves as expected, the load metric and the scaling metric must also correlate strongly with each other. • The target utilization must match the type of scaling metric. For a policy conﬁguration that uses CPU utilization, this is a target percentage. For a policy conﬁguration that uses throughput, such as the number of requests or messages, this is the target number of requests or messages per instance during any one-minute interval. • If these recommendations are not followed, the forecasted future values of the time series will probably be incorrect. To validate that the data is correct, you can view the forecasted values. Alternatively, after you create your predictive scaling policy, inspect the LoadForecast and CapacityForecast objects returned by a call to the GetPredictiveScalingForecast API. • We strongly recommend that you conﬁgure predictive scaling in forecast only mode so that you can evaluate the forecast before predictive scaling starts actively scaling capacity. Prerequisites To add custom metrics to your predictive scaling policy, you must have cloudwatch:GetMetricData permissions. To specify your own metrics instead of the metrics that AWS provides, you must ﬁrst publish your metrics to CloudWatch. For more information, see Publishing custom metrics in the Amazon CloudWatch User Guide. If you publish your own metrics, make sure to publish the data points at a minimum frequency of ﬁve minutes. Application Auto Scaling retrieves the data points from CloudWatch based on the length of the period that it needs. For example, the load metric speciﬁcation uses hourly metrics to measure the load on your application. CloudWatch uses your published metric data to provide a single data value for any one-hour period by aggregating all data points with timestamps that fall within each one-hour period. Constructing the JSON for custom metrics The following section contains examples for how to conﬁgure predictive scaling to query data from CloudWatch for Amazon EC2 Auto Scaling. There are two diﬀerent methods to conﬁgure this Prerequisites 105 Application Auto Scaling User Guide option, and the method that you choose aﬀects which format you use to construct the JSON for your predictive scaling policy. When you use metric math, the format of the JSON varies further based on the metric math being performed. 1. To create a policy that gets data directly from other CloudWatch metrics provided by AWS or metrics that you publish to CloudWatch, see Example predictive scaling policy with custom load and scaling metrics (AWS CLI). 2. To create a policy that can query multiple CloudWatch metrics and use math expressions to create new time series based
app-as-ug-036	app-as-ug.pdf	36	Scaling User Guide option, and the method that you choose aﬀects which format you use to construct the JSON for your predictive scaling policy. When you use metric math, the format of the JSON varies further based on the metric math being performed. 1. To create a policy that gets data directly from other CloudWatch metrics provided by AWS or metrics that you publish to CloudWatch, see Example predictive scaling policy with custom load and scaling metrics (AWS CLI). 2. To create a policy that can query multiple CloudWatch metrics and use math expressions to create new time series based on these metrics, see Use metric math expressions. Example predictive scaling policy with custom load and scaling metrics (AWS CLI) To create a predictive scaling policy with custom load and scaling metrics with the AWS CLI, store the arguments for --predictive-scaling-configuration in a JSON ﬁle named config.json. You start adding custom metrics by replacing the replaceable values in the following example with those of your metrics and your target utilization. { "MetricSpecifications": [ { "TargetValue": 50, "CustomizedScalingMetricSpecification": { "MetricDataQueries": [ { "Id": "scaling_metric", "MetricStat": { "Metric": { "MetricName": "MyUtilizationMetric", "Namespace": "MyNameSpace", "Dimensions": [ { "Name": "MyOptionalMetricDimensionName", "Value": "MyOptionalMetricDimensionValue" } ] }, "Stat": "Average" } } Constructing the JSON for custom metrics 106 User Guide Application Auto Scaling ] }, "CustomizedLoadMetricSpecification": { "MetricDataQueries": [ { "Id": "load_metric", "MetricStat": { "Metric": { "MetricName": "MyLoadMetric", "Namespace": "MyNameSpace", "Dimensions": [ { "Name": "MyOptionalMetricDimensionName", "Value": "MyOptionalMetricDimensionValue" } ] }, "Stat": "Sum" } } ] } } ] } For more information, see MetricDataQuery in the Amazon EC2 Auto Scaling API Reference. Note Following are some additional resources that can help you ﬁnd metric names, namespaces, dimensions, and statistics for CloudWatch metrics: • For information about the available metrics for AWS services, see AWS services that publish CloudWatch metrics in the Amazon CloudWatch User Guide. • To get the exact metric name, namespace, and dimensions (if applicable) for a CloudWatch metric with the AWS CLI, see list-metrics. To create this policy, run the put-scaling-policy command using the JSON ﬁle as input, as demonstrated in the following example. Constructing the JSON for custom metrics 107 Application Auto Scaling User Guide aws autoscaling put-scaling-policy --policy-name my-predictive-scaling-policy \ --auto-scaling-group-name my-asg --policy-type PredictiveScaling \ --predictive-scaling-configuration file://config.json If successful, this command returns the policy's Amazon Resource Name (ARN). { "PolicyARN": "arn:aws:autoscaling:region:account-id:scalingPolicy:2f4f5048-d8a8-4d14- b13a-d1905620f345:autoScalingGroupName/my-asg:policyName/my-predictive-scaling-policy", "Alarms": [] } Use metric math expressions The following section provides information and examples of predictive scaling policies that show how you can use metric math in your policy. Topics • Understand metric math • Example predictive scaling policy for Amazon EC2 Auto Scaling that combines metrics using metric math (AWS CLI) • Example predictive scaling policy to use in a blue/green deployment scenario (AWS CLI) Understand metric math If all you want to do is aggregate existing metric data, CloudWatch metric math saves you the eﬀort and cost of publishing another metric to CloudWatch. You can use any metric that AWS provides, and you can also use metrics that you deﬁne as part of your applications. For more information, see Using metric math in the Amazon CloudWatch User Guide. If you choose to use a metric math expression in your predictive scaling policy, consider the following points: • Metric math operations use the data points of the unique combination of metric name, namespace, and dimension keys/value pairs of metrics. • You can use any arithmetic operator (+ - * / ^), statistical function (such as AVG or SUM), or other function that CloudWatch supports. Constructing the JSON for custom metrics 108 Application Auto Scaling User Guide • You can use both metrics and the results of other math expressions in the formulas of the math expression. • Your metric math expressions can be made up of diﬀerent aggregations. However, it's a best practice for the ﬁnal aggregation result to use Average for the scaling metric and Sum for the load metric. • Any expressions used in a metric speciﬁcation must eventually return a single time series. To use metric math, do the following: • Choose one or more CloudWatch metrics. Then, create the expression. For more information, see Using metric math in the Amazon CloudWatch User Guide. • Verify that the metric math expression is valid by using the CloudWatch console or the CloudWatch GetMetricData API. Example predictive scaling policy for Amazon EC2 Auto Scaling that combines metrics using metric math (AWS CLI) Sometimes, instead of specifying the metric directly, you might need to ﬁrst process its data in some way. For example, you might have an application that pulls work from an Amazon SQS queue, and you might want to use the number of items in the queue as criteria for predictive scaling. The number of messages in the queue does not solely deﬁne the number of
app-as-ug-037	app-as-ug.pdf	37	Verify that the metric math expression is valid by using the CloudWatch console or the CloudWatch GetMetricData API. Example predictive scaling policy for Amazon EC2 Auto Scaling that combines metrics using metric math (AWS CLI) Sometimes, instead of specifying the metric directly, you might need to ﬁrst process its data in some way. For example, you might have an application that pulls work from an Amazon SQS queue, and you might want to use the number of items in the queue as criteria for predictive scaling. The number of messages in the queue does not solely deﬁne the number of instances that you need. Therefore, more work is needed to create a metric that can be used to calculate the backlog per instance. The following is an example predictive scaling policy for this scenario. It speciﬁes scaling and load metrics that are based on the Amazon SQS ApproximateNumberOfMessagesVisible metric, which is the number of messages available for retrieval from the queue. It also uses the Amazon EC2 Auto Scaling GroupInServiceInstances metric and a math expression to calculate the backlog per instance for the scaling metric. aws autoscaling put-scaling-policy --policy-name my-sqs-custom-metrics-policy \ --auto-scaling-group-name my-asg --policy-type PredictiveScaling \ --predictive-scaling-configuration file://config.json { "MetricSpecifications": [ { "TargetValue": 100, "CustomizedScalingMetricSpecification": { Constructing the JSON for custom metrics 109 Application Auto Scaling User Guide "MetricDataQueries": [ { "Label": "Get the queue size (the number of messages waiting to be processed)", "Id": "queue_size", "MetricStat": { "Metric": { "MetricName": "ApproximateNumberOfMessagesVisible", "Namespace": "AWS/SQS", "Dimensions": [ { "Name": "QueueName", "Value": "my-queue" } ] }, "Stat": "Sum" }, "ReturnData": false }, { "Label": "Get the group size (the number of running instances)", "Id": "running_capacity", "MetricStat": { "Metric": { "MetricName": "GroupInServiceInstances", "Namespace": "AWS/AutoScaling", "Dimensions": [ { "Name": "AutoScalingGroupName", "Value": "my-asg" } ] }, "Stat": "Sum" }, "ReturnData": false }, { "Label": "Calculate the backlog per instance", "Id": "scaling_metric", "Expression": "queue_size / running_capacity", "ReturnData": true } Constructing the JSON for custom metrics 110 Application Auto Scaling ] }, "CustomizedLoadMetricSpecification": { "MetricDataQueries": [ { "Id": "load_metric", "MetricStat": { "Metric": { "MetricName": "ApproximateNumberOfMessagesVisible", User Guide "Namespace": "AWS/SQS", "Dimensions": [ { "Name": "QueueName", "Value": "my-queue" } ], }, "Stat": "Sum" }, "ReturnData": true } ] } } ] } The example returns the policy's ARN. { "PolicyARN": "arn:aws:autoscaling:region:account-id:scalingPolicy:2f4f5048-d8a8-4d14- b13a-d1905620f345:autoScalingGroupName/my-asg:policyName/my-sqs-custom-metrics-policy", "Alarms": [] } Example predictive scaling policy to use in a blue/green deployment scenario (AWS CLI) A search expression provides an advanced option in which you can query for a metric from multiple Auto Scaling groups and perform math expressions on them. This is especially useful for blue/ green deployments. Constructing the JSON for custom metrics 111 Application Auto Scaling Note User Guide A blue/green deployment is a deployment method in which you create two separate but identical Auto Scaling groups. Only one of the groups receives production traﬃc. User traﬃc is initially directed to the earlier ("blue") Auto Scaling group, while a new group ("green") is used for testing and evaluation of a new version of an application or service. User traﬃc is shifted to the green Auto Scaling group after a new deployment is tested and accepted. You can then delete the blue group after the deployment is successful. When new Auto Scaling groups get created as part of a blue/green deployment, the metric history of each group can be automatically included in the predictive scaling policy without you having to change its metric speciﬁcations. For more information, see Using EC2 Auto Scaling predictive scaling policies with Blue/Green deployments on the AWS Compute Blog. The following example policy shows how this can be done. In this example, the policy uses the CPUUtilization metric emitted by Amazon EC2. It uses the Amazon EC2 Auto Scaling GroupInServiceInstances metric and a math expression to calculate the value of the scaling metric per instance. It also speciﬁes a capacity metric speciﬁcation to get the GroupInServiceInstances metric. The search expression ﬁnds the CPUUtilization of instances in multiple Auto Scaling groups based on the speciﬁed search criteria. If you later create a new Auto Scaling group that matches the same search criteria, the CPUUtilization of the instances in the new Auto Scaling group is automatically included. aws autoscaling put-scaling-policy --policy-name my-blue-green-predictive-scaling- policy \ --auto-scaling-group-name my-asg --policy-type PredictiveScaling \ --predictive-scaling-configuration file://config.json { "MetricSpecifications": [ { "TargetValue": 25, "CustomizedScalingMetricSpecification": { "MetricDataQueries": [ { "Id": "load_sum", "Expression": "SUM(SEARCH('{AWS/EC2,AutoScalingGroupName} MetricName= \"CPUUtilization\" ASG-myapp', 'Sum', 300))", Constructing the JSON for custom metrics 112 Application Auto Scaling User Guide "ReturnData": false }, { "Id": "capacity_sum", "Expression": "SUM(SEARCH('{AWS/AutoScaling,AutoScalingGroupName} MetricName=\"GroupInServiceInstances\" ASG-myapp', 'Average', 300))", "ReturnData": false }, { "Id": "weighted_average", "Expression": "load_sum / capacity_sum", "ReturnData": true } ] }, "CustomizedLoadMetricSpecification": { "MetricDataQueries": [ { "Id": "load_sum", "Expression": "SUM(SEARCH('{AWS/EC2,AutoScalingGroupName} MetricName= \"CPUUtilization\" ASG-myapp', 'Sum', 3600))" } ] }, "CustomizedCapacityMetricSpecification": { "MetricDataQueries": [ { "Id": "capacity_sum", "Expression": "SUM(SEARCH('{AWS/AutoScaling,AutoScalingGroupName} MetricName=\"GroupInServiceInstances\" ASG-myapp', 'Average', 300))" } ] } } ] } The example returns the policy's ARN.
app-as-ug-038	app-as-ug.pdf	38	"MetricSpecifications": [ { "TargetValue": 25, "CustomizedScalingMetricSpecification": { "MetricDataQueries": [ { "Id": "load_sum", "Expression": "SUM(SEARCH('{AWS/EC2,AutoScalingGroupName} MetricName= \"CPUUtilization\" ASG-myapp', 'Sum', 300))", Constructing the JSON for custom metrics 112 Application Auto Scaling User Guide "ReturnData": false }, { "Id": "capacity_sum", "Expression": "SUM(SEARCH('{AWS/AutoScaling,AutoScalingGroupName} MetricName=\"GroupInServiceInstances\" ASG-myapp', 'Average', 300))", "ReturnData": false }, { "Id": "weighted_average", "Expression": "load_sum / capacity_sum", "ReturnData": true } ] }, "CustomizedLoadMetricSpecification": { "MetricDataQueries": [ { "Id": "load_sum", "Expression": "SUM(SEARCH('{AWS/EC2,AutoScalingGroupName} MetricName= \"CPUUtilization\" ASG-myapp', 'Sum', 3600))" } ] }, "CustomizedCapacityMetricSpecification": { "MetricDataQueries": [ { "Id": "capacity_sum", "Expression": "SUM(SEARCH('{AWS/AutoScaling,AutoScalingGroupName} MetricName=\"GroupInServiceInstances\" ASG-myapp', 'Average', 300))" } ] } } ] } The example returns the policy's ARN. { "PolicyARN": "arn:aws:autoscaling:region:account-id:scalingPolicy:2f4f5048-d8a8-4d14- b13a-d1905620f345:autoScalingGroupName/my-asg:policyName/my-blue-green-predictive- scaling-policy", Constructing the JSON for custom metrics 113 Application Auto Scaling "Alarms": [] } User Guide Considerations for custom metrics in a predictive scaling policy If an issue occurs while using custom metrics, we recommend that you do the following: • If an error message is provided, read the message and resolve the issue it reports, if possible. • If you did not validate an expression in advance, the put-scaling-policy command validates it when you create your scaling policy. However, there is a possibility that this command might fail to identify the exact cause of the detected errors. To ﬁx the issues, troubleshoot the errors that you receive in a response from a request to the get-metric-data command. You can also troubleshoot the expression from the CloudWatch console. • You must specify false for ReturnData if MetricDataQueries speciﬁes the SEARCH() function on its own without a math function like SUM(). This is because search expressions might return multiple time series, and a metric speciﬁcation based on an expression can return only one time series. • All metrics involved in a search expression should be of the same resolution. Limitations The following limitations apply. • You can query data points of up to 10 metrics in one metric speciﬁcation. • For the purposes of this limit, one expression counts as one metric. Considerations for custom metrics 114 Application Auto Scaling User Guide Tutorial: Conﬁgure auto scaling to handle a heavy workload In this tutorial, you learn how to scale out and in based on time windows when your application will have a heavier than normal workload. This is helpful when you have an application that can suddenly have a large number of visitors on a regular schedule or on a seasonal basis. You can use a target tracking scaling policy together with scheduled scaling to handle the extra load. Scheduled scaling automatically initiates changes to your MinCapacity and MaxCapacity on your behalf, based on a schedule that you specify. When a target tracking scaling policy is active on the resource, it can scale dynamically based on current resource utilization, within the new minimum and maximum capacity range. After completing this tutorial, you’ll know how to: • Use scheduled scaling to add extra capacity to meet a heavy load before it arrives, and then remove the extra capacity when it's no longer required. • Use a target tracking scaling policy to scale your application based on current resource utilization. Contents • Prerequisites • Step 1: Register your scalable target • Step 2: Set up scheduled actions according to your requirements • Step 3: Add a target tracking scaling policy • Step 4: Next steps • Step 5: Clean up Prerequisites This tutorial assumes that you have already done the following: • Created an AWS account. Prerequisites 115 Application Auto Scaling User Guide • Installed and conﬁgured the AWS CLI. • Granted the necessary permissions for registering and deregistering resources as scalable targets with Application Auto Scaling. In addition, granted the necessary permissions for creating scaling policies and scheduled actions. For more information, see Identity and Access Management for Application Auto Scaling. • Created a supported resource in a non-production environment available to use for this tutorial. If you don't already have one, create one now. For information about the AWS services and resources that work with Application Auto Scaling, see the AWS services that you can use with Application Auto Scaling section. Note While completing this tutorial, there are two steps in which you set your resource's minimum and maximum capacity values to 0 to reset the current capacity to 0. Depending on which resource you're using with Application Auto Scaling, you might be unable to reset the current capacity to 0 during these steps. To help you address the issue, a message in the output will indicate that minimum capacity cannot be less than the value speciﬁed and will provide the minimum capacity value that the AWS resource can accept. Step 1: Register your scalable target Start by registering your resource as a scalable target with Application Auto Scaling. A scalable target is a resource that Application Auto Scaling can scale out and scale in. To register
app-as-ug-039	app-as-ug.pdf	39	capacity to 0. Depending on which resource you're using with Application Auto Scaling, you might be unable to reset the current capacity to 0 during these steps. To help you address the issue, a message in the output will indicate that minimum capacity cannot be less than the value speciﬁed and will provide the minimum capacity value that the AWS resource can accept. Step 1: Register your scalable target Start by registering your resource as a scalable target with Application Auto Scaling. A scalable target is a resource that Application Auto Scaling can scale out and scale in. To register your scalable target with Application Auto Scaling • Use the following register-scalable-target command to register a new scalable target. Set the --min-capacity and --max-capacity values to 0 to reset the current capacity to 0. Replace the sample text for --service-namespace with the namespace of the AWS service you're using with Application Auto Scaling, --scalable-dimension with the scalable dimension associated with the resource you're registering, and --resource-id with an identiﬁer for the resource. These values vary based on which resource is used and how the resource ID is constructed. See the topics in the AWS services that you can use with Application Auto Scaling section for more information. These topics include example commands that show you how to register scalable targets with Application Auto Scaling. Step 1: Register your scalable target 116 Application Auto Scaling Linux, macOS, or Unix User Guide aws application-autoscaling register-scalable-target \ --service-namespace namespace \ --scalable-dimension dimension \ --resource-id identifier \ --min-capacity 0 --max-capacity 0 Windows aws application-autoscaling register-scalable-target --service-namespace namespace --scalable-dimension dimension --resource-id identifier --min-capacity 0 --max- capacity 0 If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account- id:scalable-target/1234abcd56ab78cd901ef1234567890ab123" } Step 2: Set up scheduled actions according to your requirements You can use the put-scheduled-action command to create scheduled actions that are conﬁgured to meet your business needs. In this tutorial, we focus on a conﬁguration that stops consuming resources outside of working hours by reducing capacity to 0. To create a scheduled action that scales out in the morning 1. To scale out the scalable target, use the following put-scheduled-action command. Include the --schedule parameter with a recurring schedule, in UTC, using a cron expression. On the speciﬁed schedule (every day at 9:00 AM UTC), Application Auto Scaling updates the MinCapacity and MaxCapacity values to the desired range of 1-5 capacity units. Linux, macOS, or Unix Step 2: Set up scheduled actions according to your requirements 117 Application Auto Scaling User Guide aws application-autoscaling put-scheduled-action \ --service-namespace namespace \ --scalable-dimension dimension \ --resource-id identifier \ --scheduled-action-name my-first-scheduled-action \ --schedule "cron(0 9 * * ? )" \ --scalable-target-action MinCapacity=1,MaxCapacity=5 Windows aws application-autoscaling put-scheduled-action --service-namespace namespace -- scalable-dimension dimension --resource-id identifier --scheduled-action-name my- first-scheduled-action --schedule "cron(0 9 * ? )" --scalable-target-action MinCapacity=1,MaxCapacity=5 This command does not return any output if it is successful. 2. To conﬁrm that your scheduled action exists, use the following describe-scheduled-actions command. Linux, macOS, or Unix aws application-autoscaling describe-scheduled-actions \ --service-namespace namespace \ --query 'ScheduledActions[?ResourceId==`identifier`]' Windows aws application-autoscaling describe-scheduled-actions --service- namespace namespace --query "ScheduledActions[?ResourceId==`identifier`]" The following is example output. [ { "ScheduledActionName": "my-first-scheduled-action", "ScheduledActionARN": "arn", "Schedule": "cron(0 9 * ? )", "ScalableTargetAction": { "MinCapacity": 1, Step 2: Set up scheduled actions according to your requirements 118 Application Auto Scaling User Guide "MaxCapacity": 5 }, ... } ] To create a scheduled action that scales in at night 1. Repeat the preceding procedure to create another scheduled action that Application Auto Scaling uses to scale in at the end of the day. On the speciﬁed schedule (every day at 8:00 PM UTC), Application Auto Scaling updates the target's MinCapacity and MaxCapacity to 0, as instructed by the following put-scheduled- action command. Linux, macOS, or Unix aws application-autoscaling put-scheduled-action \ --service-namespace namespace \ --scalable-dimension dimension \ --resource-id identifier \ --scheduled-action-name my-second-scheduled-action \ --schedule "cron(0 20 * ? )" \ --scalable-target-action MinCapacity=0,MaxCapacity=0 Windows aws application-autoscaling put-scheduled-action --service-namespace namespace -- scalable-dimension dimension --resource-id identifier --scheduled-action-name my- second-scheduled-action --schedule "cron(0 20 * ? )" --scalable-target-action MinCapacity=0,MaxCapacity=0 2. To conﬁrm that your scheduled action exists, use the following describe-scheduled-actions command. Linux, macOS, or Unix aws application-autoscaling describe-scheduled-actions \ --service-namespace namespace \ --query 'ScheduledActions[?ResourceId==`identifier`]' Step 2: Set up scheduled actions according to your requirements 119 Application Auto Scaling Windows User Guide aws application-autoscaling describe-scheduled-actions --service- namespace namespace --query "ScheduledActions[?ResourceId==`identifier`]" The following is example output. [ { "ScheduledActionName": "my-first-scheduled-action", "ScheduledActionARN": "arn", "Schedule": "cron(0 9 * ? )", "ScalableTargetAction": { "MinCapacity": 1, "MaxCapacity": 5 }, ... }, { "ScheduledActionName": "my-second-scheduled-action", "ScheduledActionARN": "arn", "Schedule": "cron(0 20 * ? *)", "ScalableTargetAction": { "MinCapacity": 0, "MaxCapacity": 0 }, ... } ] Step 3: Add a target tracking scaling policy Now that you have the basic schedule in place, add a target tracking scaling policy to
app-as-ug-040	app-as-ug.pdf	40	--query 'ScheduledActions[?ResourceId==`identifier`]' Step 2: Set up scheduled actions according to your requirements 119 Application Auto Scaling Windows User Guide aws application-autoscaling describe-scheduled-actions --service- namespace namespace --query "ScheduledActions[?ResourceId==`identifier`]" The following is example output. [ { "ScheduledActionName": "my-first-scheduled-action", "ScheduledActionARN": "arn", "Schedule": "cron(0 9 * * ? )", "ScalableTargetAction": { "MinCapacity": 1, "MaxCapacity": 5 }, ... }, { "ScheduledActionName": "my-second-scheduled-action", "ScheduledActionARN": "arn", "Schedule": "cron(0 20 * ? *)", "ScalableTargetAction": { "MinCapacity": 0, "MaxCapacity": 0 }, ... } ] Step 3: Add a target tracking scaling policy Now that you have the basic schedule in place, add a target tracking scaling policy to scale based on current resource utilization. With target tracking, Application Auto Scaling compares the target value in the policy to the current value of the speciﬁed metric. When they are unequal for a period of time, Application Auto Scaling adds or removes capacity to maintain steady performance. As the load on your application and the metric value increases, Application Auto Scaling adds capacity as fast as it can without Step 3: Add a target tracking scaling policy 120 Application Auto Scaling User Guide going above MaxCapacity. When Application Auto Scaling removes capacity because the load is minimal, it does so without going below MinCapacity. By adjusting the capacity based on usage, you only pay for what your application needs. If the metric has insuﬃcient data because your application does not have any load, Application Auto Scaling does not add or remove capacity. In other words, Application Auto Scaling prioritizes availability in situations where not enough information is available. You can add multiple scaling policies, but make sure you do not add conﬂicting step scaling policies, which might cause undesirable behavior. For example, if the step scaling policy initiates a scale-in activity before the target tracking policy is ready to scale in, the scale-in activity will not be blocked. After the scale-in activity completes, the target tracking policy could instruct Application Auto Scaling to scale out again. To create a target tracking scaling policy 1. Use the following put-scaling-policy command to create the policy. The metrics that are most frequently used for target tracking are predeﬁned, and you can use them without supplying the full metric speciﬁcation from CloudWatch. For more information about the available predeﬁned metrics, see Target tracking scaling policies for Application Auto Scaling. Before you run this command, make sure that your predeﬁned metric expects the target value. For example, to scale out when CPU reaches 50% utilization, specify a target value of 50.0. Or, to scale out Lambda provisioned concurrency when usage reaches 70% utilization, specify a target value of 0.7. For information about target values for a particular resource, refer to the documentation that is provided by the service about how to conﬁgure target tracking. For more information, see AWS services that you can use with Application Auto Scaling. Linux, macOS, or Unix aws application-autoscaling put-scaling-policy \ --service-namespace namespace \ --scalable-dimension dimension \ --resource-id identifier \ --policy-name my-scaling-policy --policy-type TargetTrackingScaling \ --target-tracking-scaling-policy-configuration '{ "TargetValue": 50.0, "PredefinedMetricSpecification": { "PredefinedMetricType": "predefinedmetric" }}' Step 3: Add a target tracking scaling policy 121 Application Auto Scaling Windows User Guide aws application-autoscaling put-scaling-policy --service-namespace namespace -- scalable-dimension dimension --resource-id identifier --policy-name my-scaling- policy --policy-type TargetTrackingScaling --target-tracking-scaling-policy- configuration "{ \"TargetValue\": 50.0, \"PredefinedMetricSpecification\": { \"PredefinedMetricType\": \"predefinedmetric\" }}" If successful, this command returns the ARNs and names of the two CloudWatch alarms that were created on your behalf. 2. To conﬁrm that your scheduled action exists, use the following describe-scaling-policies command. Linux, macOS, or Unix aws application-autoscaling describe-scaling-policies --service-namespace namespace \ --query 'ScalingPolicies[?ResourceId==`identifier`]' Windows aws application-autoscaling describe-scaling-policies --service-namespace namespace --query "ScalingPolicies[?ResourceId==`identifier`]" The following is example output. [ { "PolicyARN": "arn", "TargetTrackingScalingPolicyConfiguration": { "PredefinedMetricSpecification": { "PredefinedMetricType": "predefinedmetric" }, "TargetValue": 50.0 }, "PolicyName": "my-scaling-policy", "PolicyType": "TargetTrackingScaling", "Alarms": [], ... } Step 3: Add a target tracking scaling policy 122 Application Auto Scaling ] Step 4: Next steps User Guide When a scaling activity occurs, you see a record of it in the output of the scaling activities for the scalable target, for example: Successfully set desired count to 1. Change successfully fulfilled by ecs. To monitor your scaling activities with Application Auto Scaling, you can use the following describe-scaling-activities command. Linux, macOS, or Unix aws application-autoscaling describe-scaling-activities --service-namespace namespace \ --scalable-dimension dimension \ --resource-id identifier Windows aws application-autoscaling describe-scaling-activities --service-namespace namespace --scalable-dimension dimension --resource-id identifier Step 5: Clean up To prevent your account from accruing charges for resources created while actively scaling, you can clean up the associated scaling conﬁguration as follows. Deleting the scaling conﬁguration does not delete the underlying AWS resource. It also does not return it to its original capacity. You can use the console of the service where you created the resource to delete it or adjust its capacity. To delete the scheduled actions The following delete-scheduled-action command deletes a speciﬁed scheduled
app-as-ug-041	app-as-ug.pdf	41	describe-scaling-activities --service-namespace namespace \ --scalable-dimension dimension \ --resource-id identifier Windows aws application-autoscaling describe-scaling-activities --service-namespace namespace --scalable-dimension dimension --resource-id identifier Step 5: Clean up To prevent your account from accruing charges for resources created while actively scaling, you can clean up the associated scaling conﬁguration as follows. Deleting the scaling conﬁguration does not delete the underlying AWS resource. It also does not return it to its original capacity. You can use the console of the service where you created the resource to delete it or adjust its capacity. To delete the scheduled actions The following delete-scheduled-action command deletes a speciﬁed scheduled action. You can skip this step if you want to keep the scheduled actions that you created. Step 4: Next steps 123 Application Auto Scaling Linux, macOS, or Unix User Guide aws application-autoscaling delete-scheduled-action \ --service-namespace namespace \ --scalable-dimension dimension \ --resource-id identifier \ --scheduled-action-name my-second-scheduled-action Windows aws application-autoscaling delete-scheduled-action --service-namespace namespace --scalable-dimension dimension --resource-id identifier --scheduled-action-name my- second-scheduled-action To delete the scaling policy The following delete-scaling-policy command deletes a speciﬁed target tracking scaling policy. You can skip this step if you want to keep the scaling policy that you created. Linux, macOS, or Unix aws application-autoscaling delete-scaling-policy \ --service-namespace namespace \ --scalable-dimension dimension \ --resource-id identifier \ --policy-name my-scaling-policy Windows aws application-autoscaling delete-scaling-policy --service-namespace namespace -- scalable-dimension dimension --resource-id identifier --policy-name my-scaling-policy To deregister the scalable target Use the following deregister-scalable-target command to deregister the scalable target. If you have any scaling policies that you created or any scheduled actions that have not yet been deleted, they are deleted by this command. You can skip this step if you want to keep the scalable target registered for future use. Linux, macOS, or Unix Step 5: Clean up 124 Application Auto Scaling User Guide aws application-autoscaling deregister-scalable-target \ --service-namespace namespace \ --scalable-dimension dimension \ --resource-id identifier Windows aws application-autoscaling deregister-scalable-target --service-namespace namespace -- scalable-dimension dimension --resource-id identifier Step 5: Clean up 125 Application Auto Scaling User Guide Suspend and resume scaling for Application Auto Scaling This topic explains how to suspend and then resume one or more of the scaling activities for the scalable targets in your application. The suspend-resume feature is used to temporarily pause scaling activities triggered by your scaling policies and scheduled actions. This can be useful, for example, when you don't want automatic scaling to potentially interfere while you are making a change or investigating a conﬁguration issue. Your scaling policies and scheduled actions can be retained, and when you are ready, scaling activities can be resumed. In the example CLI commands that follow, you pass the JSON-formatted parameters in a conﬁg.json ﬁle. You can also pass these parameters on the command line by using quotation marks to enclose the JSON data structure. For more information, see Using quotation marks with strings in the AWS CLI in the AWS Command Line Interface User Guide. Contents • Scaling activities • Suspend and resume scaling activities Note For instructions for suspending scale-out processes while Amazon ECS deployments are in progress, see the following documentation: Service auto scaling and deployments in the Amazon Elastic Container Service Developer Guide Scaling activities Application Auto Scaling supports putting the following scaling activities in a suspended state: • All scale-in activities that are triggered by a scaling policy. • All scale-out activities that are triggered by a scaling policy. • All scaling activities that involve scheduled actions. Scaling activities 126 Application Auto Scaling User Guide The following descriptions explain what happens when individual scaling activities are suspended. Each one can be suspended and resumed independently. Depending on the reason for suspending a scaling activity, you might need to suspend multiple scaling activities together. DynamicScalingInSuspended • Application Auto Scaling does not remove capacity when a target tracking scaling policy or a step scaling policy is triggered. This allows you to temporarily disable scale-in activities associated with scaling policies without deleting the scaling policies or their associated CloudWatch alarms. When you resume scale in, Application Auto Scaling evaluates policies with alarm thresholds that are currently in breach. DynamicScalingOutSuspended • Application Auto Scaling does not add capacity when a target tracking scaling policy or a step scaling policy is triggered. This allows you to temporarily disable scale-out activities associated with scaling policies without deleting the scaling policies or their associated CloudWatch alarms. When you resume scale out, Application Auto Scaling evaluates policies with alarm thresholds that are currently in breach. ScheduledScalingSuspended • Application Auto Scaling does not initiate the scaling actions that are scheduled to run during the suspension period. When you resume scheduled scaling, Application Auto Scaling only evaluates scheduled actions whose execution time has not yet passed. Suspend and resume scaling activities You can suspend and resume individual scaling activities or all scaling activities for your Application Auto Scaling scalable target. Note For brevity,
app-as-ug-042	app-as-ug.pdf	42	scale-out activities associated with scaling policies without deleting the scaling policies or their associated CloudWatch alarms. When you resume scale out, Application Auto Scaling evaluates policies with alarm thresholds that are currently in breach. ScheduledScalingSuspended • Application Auto Scaling does not initiate the scaling actions that are scheduled to run during the suspension period. When you resume scheduled scaling, Application Auto Scaling only evaluates scheduled actions whose execution time has not yet passed. Suspend and resume scaling activities You can suspend and resume individual scaling activities or all scaling activities for your Application Auto Scaling scalable target. Note For brevity, these examples illustrate how to suspend and resume scaling for a DynamoDB table. To specify a diﬀerent scalable target, specify its namespace in --service- namespace, its scalable dimension in --scalable-dimension, and its resource ID in -- Suspend and resume scaling activities 127 Application Auto Scaling User Guide resource-id. For more information and examples for each service, see the topics in AWS services that you can use with Application Auto Scaling. To suspend a scaling activity Open a command-line window and use the register-scalable-target command with the -- suspended-state option as follows. Linux, macOS, or Unix aws application-autoscaling register-scalable-target --service-namespace dynamodb \ --scalable-dimension dynamodb:table:ReadCapacityUnits --resource-id table/my-table \ --suspended-state file://config.json Windows aws application-autoscaling register-scalable-target --service-namespace dynamodb -- scalable-dimension dynamodb:table:ReadCapacityUnits --resource-id table/my-table -- suspended-state file://config.json If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } To only suspend scale-in activities that are triggered by a scaling policy, specify the following in conﬁg.json. { "DynamicScalingInSuspended":true } To only suspend scale-out activities that are triggered by a scaling policy, specify the following in conﬁg.json. { Suspend and resume scaling activities 128 Application Auto Scaling User Guide "DynamicScalingOutSuspended":true } To only suspend scaling activities that involve scheduled actions, specify the following in conﬁg.json. { "ScheduledScalingSuspended":true } To suspend all scaling activities Use the register-scalable-target command with the --suspended-state option as follows. Linux, macOS, or Unix aws application-autoscaling register-scalable-target --service-namespace dynamodb \ --scalable-dimension dynamodb:table:ReadCapacityUnits --resource-id table/my-table \ --suspended-state file://config.json Windows aws application-autoscaling register-scalable-target --service-namespace dynamodb -- scalable-dimension dynamodb:table:ReadCapacityUnits --resource-id table/my-table -- suspended-state file://config.json This example assumes that the ﬁle conﬁg.json contains the following JSON-formatted parameters. { "DynamicScalingInSuspended":true, "DynamicScalingOutSuspended":true, "ScheduledScalingSuspended":true } If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Suspend and resume scaling activities 129 Application Auto Scaling User Guide View suspended scaling activities Use the describe-scalable-targets command to determine which scaling activities are in a suspended state for a scalable target. Linux, macOS, or Unix aws application-autoscaling describe-scalable-targets --service-namespace dynamodb \ --scalable-dimension dynamodb:table:ReadCapacityUnits --resource-id table/my-table Windows aws application-autoscaling describe-scalable-targets --service-namespace dynamodb -- scalable-dimension dynamodb:table:ReadCapacityUnits --resource-id table/my-table The following is example output. { "ScalableTargets": [ { "ServiceNamespace": "dynamodb", "ScalableDimension": "dynamodb:table:ReadCapacityUnits", "ResourceId": "table/my-table", "MinCapacity": 1, "MaxCapacity": 20, "SuspendedState": { "DynamicScalingOutSuspended": true, "DynamicScalingInSuspended": true, "ScheduledScalingSuspended": true }, "CreationTime": 1558125758.957, "RoleARN": "arn:aws:iam::123456789012:role/aws- service-role/dynamodb.application-autoscaling.amazonaws.com/ AWSServiceRoleForApplicationAutoScaling_DynamoDBTable" } ] } Resume scaling activities When you are ready to resume the scaling activity, you can resume it using the register-scalable- target command. View suspended scaling activities 130 Application Auto Scaling User Guide The following example command resumes all scaling activities for the speciﬁed scalable target. Linux, macOS, or Unix aws application-autoscaling register-scalable-target --service-namespace dynamodb \ --scalable-dimension dynamodb:table:ReadCapacityUnits --resource-id table/my-table \ --suspended-state file://config.json Windows aws application-autoscaling register-scalable-target --service-namespace dynamodb -- scalable-dimension dynamodb:table:ReadCapacityUnits --resource-id table/my-table -- suspended-state file://config.json This example assumes that the ﬁle conﬁg.json contains the following JSON-formatted parameters. { "DynamicScalingInSuspended":false, "DynamicScalingOutSuspended":false, "ScheduledScalingSuspended":false } If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Resume scaling activities 131 Application Auto Scaling User Guide Scaling activities for Application Auto Scaling Application Auto Scaling monitors your scaling policy's CloudWatch metrics and initiates a scaling activity when thresholds are exceeded. It also initiates scaling activities when you modify the maximum or minimum size of the scalable target, either manually or following a schedule. When a scaling activity occurs, Application Auto Scaling does one of the following: • Increases the capacity of the scalable target (referred to as scaling out) • Decreases the capacity of the scalable target (referred to as scaling in) You can look up scaling activities from the last six weeks. Look up scaling activities by scalable target To see the scaling activities for a speciﬁc scalable target, use the following describe-scaling- activities command. Linux, macOS, or Unix aws application-autoscaling describe-scaling-activities --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount --resource-id service/my-cluster/my- service Windows aws application-autoscaling describe-scaling-activities --service-namespace ecs -- scalable-dimension ecs:service:DesiredCount --resource-id service/my-cluster/my-service The following is an example response, where StatusCode contains the current status of the activity and StatusMessage contains information about the status of the scaling activity. { "ScalingActivities": [ { "ScalableDimension": "ecs:service:DesiredCount", "Description": "Setting desired count to 1.", "ResourceId": "service/my-cluster/my-service", Look up scaling activities by scalable target 132 Application Auto Scaling User Guide "ActivityId": "e6c5f7d1-dbbb-4a3f-89b2-51f33e766399", "StartTime": 1462575838.171,
app-as-ug-043	app-as-ug.pdf	43	target To see the scaling activities for a speciﬁc scalable target, use the following describe-scaling- activities command. Linux, macOS, or Unix aws application-autoscaling describe-scaling-activities --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount --resource-id service/my-cluster/my- service Windows aws application-autoscaling describe-scaling-activities --service-namespace ecs -- scalable-dimension ecs:service:DesiredCount --resource-id service/my-cluster/my-service The following is an example response, where StatusCode contains the current status of the activity and StatusMessage contains information about the status of the scaling activity. { "ScalingActivities": [ { "ScalableDimension": "ecs:service:DesiredCount", "Description": "Setting desired count to 1.", "ResourceId": "service/my-cluster/my-service", Look up scaling activities by scalable target 132 Application Auto Scaling User Guide "ActivityId": "e6c5f7d1-dbbb-4a3f-89b2-51f33e766399", "StartTime": 1462575838.171, "ServiceNamespace": "ecs", "EndTime": 1462575872.111, "Cause": "monitor alarm web-app-cpu-lt-25 in state ALARM triggered policy web-app-cpu-lt-25", "StatusMessage": "Successfully set desired count to 1. Change successfully fulfilled by ecs.", "StatusCode": "Successful" } ] } For a description of the ﬁelds in the response, see ScalingActivity in the Application Auto Scaling API Reference. The following status codes indicate when the scaling event that leads to the scaling activity reaches a completed state: • Successful – Scaling was completed successfully • Overridden – The desired capacity was updated by a newer scaling event • Unfulfilled – Scaling timed out or the target service cannot fulﬁll the request • Failed – Scaling failed with an exception Note The scaling activity might also have a status of Pending or InProgress. All scaling activities have a Pending status before the target service responds. After the target responds, the status of the scaling activity changes to InProgress. Include not scaled activities By default, the scaling activities do not reﬂect times when Application Auto Scaling makes a decision about whether to not scale. For example, suppose that an Amazon ECS service exceeds the maximum threshold of a given metric, but the number of tasks is already at the maximum number of allowed tasks. In this case, Application Auto Scaling does not scale out the desired number of tasks. Include not scaled activities 133 Application Auto Scaling User Guide To include activities that aren't scaled (not scaled activities) in the response, add the --include- not-scaled-activities option to the describe-scaling-activities command. Linux, macOS, or Unix aws application-autoscaling describe-scaling-activities --include-not-scaled-activities \ --service-namespace ecs --scalable-dimension ecs:service:DesiredCount \ --resource-id service/my-cluster/my-service Windows aws application-autoscaling describe-scaling-activities --include-not-scaled-activities --service-namespace ecs --scalable-dimension ecs:service:DesiredCount --resource- id service/my-cluster/my-service Note If this command throws an error, make sure that you have updated the AWS CLI locally to the latest version. To conﬁrm that the response includes the not scaled activities, the NotScaledReasons element is shown in the output for some, if not all, failed scaling activities. { "ScalingActivities": [ { "ScalableDimension": "ecs:service:DesiredCount", "Description": "Attempting to scale due to alarm triggered", "ResourceId": "service/my-cluster/my-service", "ActivityId": "4d759079-a31f-4d0c-8468-504c56e2eecf", "StartTime": 1664928867.915, "ServiceNamespace": "ecs", "Cause": "monitor alarm web-app-cpu-gt-75 in state ALARM triggered policy web-app-cpu-gt-75", "StatusCode": "Failed", "NotScaledReasons": [ { "Code": "AlreadyAtMaxCapacity", "MaxCapacity": 4 Include not scaled activities 134 Application Auto Scaling } ] } ] } User Guide For a description of the ﬁelds in the response, see ScalingActivity in the Application Auto Scaling API Reference. If a not scaled activity is returned, depending on the reason code listed in Code, attributes like CurrentCapacity, MaxCapacity, and MinCapacity might be present in the response. To prevent large quantities of duplicate entries, only the ﬁrst not scaled activity will be recorded in the scaling activity history. Any subsequent not scaled activities will not generate new entries unless the reason for not scaling changes. Reason codes The following are the reason codes for a not scaled activity. Reason code Deﬁnition AutoScali ngAnticip Auto scaling algorithm decided atedFlapp not to take a ing scaling action because it would lead to ﬂapping. Flapping is an inﬁnite loop of scaling in and scaling out. That is, if a scaling action is taken, the metric value would change to start another Reason codes 135 User Guide Application Auto Scaling Reason code Deﬁnition scaling action in the reverse direction. TargetSer vicePutRe The target service has temporarily sourceAsU put the resource nscalable in an unscalabl e state. Applicati on Auto Scaling will attempt to scale again when the automatic scaling condition s speciﬁed in the scaling policy are met. AlreadyAt MaxCapaci Scaling is blocked by the maximum ty capacity that you speciﬁed. If you want Application Auto Scaling to scale out, you need to increase the maximum capacity. Reason codes 136 User Guide Application Auto Scaling Reason code Deﬁnition AlreadyAt MinCapaci Scaling is blocked by the minimum ty capacity that you speciﬁed. If you want Application Auto Scaling to scale in, you need to decrease the minimum capacity. AlreadyAt DesiredCa Auto scaling algorithm pacity calculated the revised capacity to be equal to the current capacity. Reason codes 137 Application Auto Scaling User Guide Monitoring Application Auto Scaling Monitoring is an important part of maintaining the reliability, availability, and performance of Application Auto Scaling and your other AWS
app-as-ug-044	app-as-ug.pdf	44	Scaling to scale out, you need to increase the maximum capacity. Reason codes 136 User Guide Application Auto Scaling Reason code Deﬁnition AlreadyAt MinCapaci Scaling is blocked by the minimum ty capacity that you speciﬁed. If you want Application Auto Scaling to scale in, you need to decrease the minimum capacity. AlreadyAt DesiredCa Auto scaling algorithm pacity calculated the revised capacity to be equal to the current capacity. Reason codes 137 Application Auto Scaling User Guide Monitoring Application Auto Scaling Monitoring is an important part of maintaining the reliability, availability, and performance of Application Auto Scaling and your other AWS solutions. You should collect monitoring data from all parts of your AWS solution so that you can more easily debug a multi-point failure if one occurs. AWS provides monitoring tools to watch Application Auto Scaling, report when something is wrong, and take automatic actions when appropriate. You can use the following features to help you manage your AWS resources: AWS CloudTrail With AWS CloudTrail, you can track the calls made to the Application Auto Scaling API by or on behalf of your AWS account. CloudTrail stores the information in log ﬁles in the Amazon S3 bucket that you specify. You can identify which users and accounts called Application Auto Scaling, the source IP address from which the calls were made, and when the calls occurred. For more information, see Log Application Auto Scaling API calls using AWS CloudTrail. Note For information about other AWS services that can help you log and collect data about your workloads, see the Logging and monitoring guide for application owners guide in the AWS Prescriptive Guidance. Amazon CloudWatch Amazon CloudWatch helps you analyze logs and, in real time, monitor the metrics of your AWS resources and hosted applications. You can collect and track metrics, create customized dashboards, and set alarms that notify you or take actions when a speciﬁed metric reaches a threshold that you specify. For example, you can have CloudWatch track resource utilization and notify you when utilization is very high or when the metric's alarm has gone into the INSUFFICIENT_DATA state. For more information, see Monitor usage of scalable resources using CloudWatch. CloudWatch also tracks AWS API usage metrics for Application Auto Scaling. You can use these metrics to conﬁgure alarms that alert you when your API call volume violates a threshold that you deﬁne. For more information, see AWS usage metrics in the Amazon CloudWatch User Guide. 138 Application Auto Scaling Amazon EventBridge User Guide Amazon EventBridge is a serverless event bus service that makes it easy to connect your applications with data from a variety of sources. EventBridge delivers a stream of real-time data from your own applications, Software-as-a-Service (SaaS) applications, and AWS services and routes that data to targets such as Lambda. This lets you monitor events that happen in services, and build event-driven architectures. For more information, see Monitor Application Auto Scaling events using Amazon EventBridge. AWS Health Dashboard The AWS Health Dashboard (PHD) displays information, and also provides notiﬁcations that are invoked by changes in the health of AWS resources. The information is presented in two ways: on a dashboard that shows recent and upcoming events organized by category, and in a full event log that shows all events from the past 90 days. For more information, see Getting started with your AWS Health Dashboard. Monitor usage of scalable resources using CloudWatch With Amazon CloudWatch, you get nearly continuous visibility into your applications across scalable resources. CloudWatch is a monitoring service for AWS resources. You can use CloudWatch to collect and track metrics, set alarms, and automatically react to changes in your AWS resources. You can also create dashboards to monitor the speciﬁc metrics or sets of metrics you need. When you interact with the services that integrate with Application Auto Scaling, they send the metrics shown in the following table to CloudWatch. In CloudWatch, metrics are grouped ﬁrst by the service namespace, and then by the various dimension combinations within each namespace. These metrics can help you monitor resource usage and plan capacity for your applications. If your application's workload is not constant, this indicates that you should consider using auto scaling. For detailed descriptions of these metrics, see the documentation for the metric of interest. Contents • CloudWatch metrics for monitoring resource usage • Predeﬁned metrics for target tracking scaling policies Monitor using CloudWatch 139 Application Auto Scaling User Guide CloudWatch metrics for monitoring resource usage The following table lists the CloudWatch metrics that are available to support monitoring resource usage. The list is not exhaustive but will give you a good starting point. If you do not see these metrics in the CloudWatch console, make sure that you have completed the set up of the resource. For more information, see the Amazon CloudWatch User
app-as-ug-045	app-as-ug.pdf	45	these metrics, see the documentation for the metric of interest. Contents • CloudWatch metrics for monitoring resource usage • Predeﬁned metrics for target tracking scaling policies Monitor using CloudWatch 139 Application Auto Scaling User Guide CloudWatch metrics for monitoring resource usage The following table lists the CloudWatch metrics that are available to support monitoring resource usage. The list is not exhaustive but will give you a good starting point. If you do not see these metrics in the CloudWatch console, make sure that you have completed the set up of the resource. For more information, see the Amazon CloudWatch User Guide. Scalable resource Namespace CloudWatc Link to documentation h metric AppStream 2.0 Fleets Fleets Aurora Replicas AWS/ AppSt ream Name: Available Capacity AppStream 2.0 metrics AppStream 2.0 metrics Aurora cluster-level metrics AWS/ AppSt ream AWS/ RDS Dimension : Fleet Name: CapacityU tilization Dimension : Fleet Name: CPUUtiliz ation Dimension s: DBCluster Identiﬁe r, Role (READER) CloudWatch metrics for monitoring resource usage 140 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation Aurora cluster-level metrics Amazon Comprehend endpoint metrics Amazon Comprehend endpoint metrics Replicas AWS/ RDS Amazon Comprehen d Document classiﬁc ation endpoints AWS/ Compr ehend Entity recognizer endpoints AWS/ Compr ehend h metric Name: DatabaseC onnection s Dimension s: DBCluster Identiﬁe r, Role (READER) Name: Inference Utilizati on Dimension : EndpointA rn Name: Inference Utilizati on Dimension : EndpointA rn CloudWatch metrics for monitoring resource usage 141 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation DynamoDB Tables and global secondary indexes AWS/ Dynam oDB Tables and global secondary indexes AWS/ Dynam oDB DynamoDB metrics DynamoDB metrics h metric Name: Provision edReadCap acityUnit s Dimension s: TableName , GlobalSec ondaryInd exName Name: Provision edWriteCa pacityUni ts Dimension s: TableName , GlobalSec ondaryInd exName CloudWatch metrics for monitoring resource usage 142 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation Tables and global secondary indexes AWS/ Dynam oDB Tables and global secondary indexes AWS/ Dynam oDB Amazon ECS h metric Name: ConsumedR DynamoDB metrics eadCapaci tyUnits Dimension s: TableName , GlobalSec ondaryInd exName Name: ConsumedW DynamoDB metrics riteCapac ityUnits Dimension s: TableName , GlobalSec ondaryInd exName CloudWatch metrics for monitoring resource usage 143 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation h metric Services AWS/ECS Name: Amazon ECS metrics CPUUtiliz ation Dimension s: ClusterNa me, ServiceNa me Services AWS/ECS Name: Amazon ECS metrics MemoryUti lization Dimension s: ClusterNa me, ServiceNa me Name: RequestCo untPerTar get Dimension : TargetGro up Services AWS/ Appli cationELB ElastiCache Application Load Balancer metrics CloudWatch metrics for monitoring resource usage 144 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation ElastiCache Valkey and Redis OSS metrics ElastiCache Valkey and Redis OSS metrics Clusters (replication groups) AWS/ Elast iCache Clusters (replication groups) AWS/ Elast iCache h metric Name: DatabaseM emoryUsag eCountedF orEvictPe rcentage Dimension : Replicati onGroupId Name: DatabaseC apacityUs ageCounte dForEvict Percentag e Dimension : Replicati onGroupId CloudWatch metrics for monitoring resource usage 145 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation Clusters (replication groups) AWS/ Elast iCache Clusters (replication groups) AWS/ Elast iCache Clusters (cache) AWS/ Elast iCache ElastiCache Valkey and Redis OSS metrics ElastiCache Valkey and Redis OSS metrics ElastiCache Memcached metrics h metric Name: EngineCPU Utilizati on Dimension s: Replicati onGroupId , Role (Primary) Name: EngineCPU Utilizati on Dimension s: Replicati onGroupId , Role (Replica) Name: EngineCPU Utilizati on Dimension s: CacheClus terId, Node CloudWatch metrics for monitoring resource usage 146 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation ElastiCache Memcached metrics Clusters (cache) AWS/ Elast iCache h metric Name: DatabaseC apacityMe moryUsage Percentag e Dimension s: CacheClus terId Amazon EMR Clusters Amazon Keyspaces AWS/ Elast Name: YARNMemor Amazon EMR metrics icMapRedu yAvailabl ce ePercenta ge Dimension : ClusterId CloudWatch metrics for monitoring resource usage 147 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation Tables Tables Tables AWS/ Cassa ndra AWS/ Cassa ndra AWS/ Cassa ndra Amazon Keyspaces metrics Amazon Keyspaces metrics h metric Name: Provision edReadCap acityUnit s Dimension s: Keyspace, TableName Name: Provision edWriteCa pacityUni ts Dimension s: Keyspace, TableName Name: ConsumedR Amazon Keyspaces metrics eadCapaci tyUnits Dimension s: Keyspace, TableName CloudWatch metrics for monitoring resource usage 148 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation Tables AWS/ Cassa ndra Lambda Provisioned concurrency AWS/ Lambda Amazon MSK Broker storage AWS/ Kafka h metric Name: ConsumedW Amazon Keyspaces metrics riteCapac ityUnits Dimension s: Keyspace, TableName Name: Provision edConcurr encyUtili zation Dimension s: FunctionN ame, Resource Name: KafkaData LogsDiskU sed Dimension s: Cluster Name Lambda function metrics Amazon MSK metrics CloudWatch metrics for monitoring resource usage 149 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation Broker storage AWS/
app-as-ug-046	app-as-ug.pdf	46	metrics eadCapaci tyUnits Dimension s: Keyspace, TableName CloudWatch metrics for monitoring resource usage 148 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation Tables AWS/ Cassa ndra Lambda Provisioned concurrency AWS/ Lambda Amazon MSK Broker storage AWS/ Kafka h metric Name: ConsumedW Amazon Keyspaces metrics riteCapac ityUnits Dimension s: Keyspace, TableName Name: Provision edConcurr encyUtili zation Dimension s: FunctionN ame, Resource Name: KafkaData LogsDiskU sed Dimension s: Cluster Name Lambda function metrics Amazon MSK metrics CloudWatch metrics for monitoring resource usage 149 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation Broker storage AWS/ Kafka Amazon MSK metrics h metric Name: KafkaData LogsDiskU sed Dimension s: Cluster Name, Broker ID Neptune Clusters AWS/ Neptune Name: CPUUtiliz Neptune metrics ation Dimension s: DBCluster Identiﬁe r, Role (READER) SageMaker AI CloudWatch metrics for monitoring resource usage 150 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation Endpoint variants AWS/ SageM aker Inference component s AWS/ SageM aker Provisioned concurrency for a AWS/ SageM serverless endpoint aker Invocation metrics Invocation metrics Serverless endpoint metrics h metric Name: Invocatio nsPerInst ance Dimension s: EndpointN ame, VariantNa me Name: Invocatio nsPerCopy Dimension s: Inference Component Name Name: Serverles sProvisio nedConcur rencyUtil ization Dimension s: EndpointN ame, VariantNa me CloudWatch metrics for monitoring resource usage 151 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation h metric Spot Fleet (Amazon EC2) Spot Fleets AWS/ EC2Spot Name: CPUUtiliz Spot Fleet metrics ation Dimension : FleetRequ estId Spot Fleets AWS/ EC2Spot Name: NetworkIn Spot Fleet metrics Dimension : FleetRequ estId Spot Fleets AWS/ EC2Spot Name: NetworkOu Spot Fleet metrics t Dimension : FleetRequ estId CloudWatch metrics for monitoring resource usage 152 Application Auto Scaling User Guide Scalable resource Namespace CloudWatc Link to documentation Application Load Balancer metrics Spot Fleets h metric AWS/ Appli Name: RequestCo cationELB untPerTar get Dimension : TargetGro up Predeﬁned metrics for target tracking scaling policies The following table lists the predeﬁned metric types from the Application Auto Scaling API Reference with their corresponding CloudWatch metric name. Each predeﬁned metric represents an aggregation of the values of the underlying CloudWatch metric. The result is the average resource usage over a one-minute period, based on a percentage unless otherwise noted. The predeﬁned metrics are only used within the context of setting up target tracking scaling policies. You can ﬁnd more information about these metrics in the service's documentation that's available from the table in CloudWatch metrics for monitoring resource usage. Predeﬁned metric type CloudWatch metric name AppStream 2.0 AppStreamAverageCapacityUti CapacityUtilization lization Aurora RDSReaderAverageCPUUtilization CPUUtilization RDSReaderAverageDatabaseCon DatabaseConnections¹ nections Predeﬁned metrics for target tracking scaling policies 153 Application Auto Scaling User Guide Predeﬁned metric type CloudWatch metric name Amazon Comprehend ComprehendInferenceUtilization InferenceUtilization DynamoDB DynamoDBReadCapacityUtilization DynamoDBWriteCapacityUtilization Amazon ECS ProvisionedReadCapacityUnits, ConsumedR eadCapacityUnits² ProvisionedWriteCapacityUnits, ConsumedW riteCapacityUnits² ECSServiceAverageCPUUtilization CPUUtilization ECSServiceAverageMemoryUtil MemoryUtilization ization ALBRequestCountPerTarget RequestCountPerTarget¹ ElastiCache ElastiCacheDatabaseMemoryUs ageCountedForEvictPercentage DatabaseMemoryUsageCountedForEvictPe rcentage ElastiCacheDatabaseCapacity UsageCountedForEvictPercentage DatabaseCapacityUsageCountedForEvict Percentage ElastiCachePrimaryEngineCPU EngineCPUUtilization Utilization ElastiCacheReplicaEngineCPU EngineCPUUtilization Utilization ElastiCacheEngineCPUUtilization EngineCPUUtilization Predeﬁned metrics for target tracking scaling policies 154 Application Auto Scaling User Guide Predeﬁned metric type CloudWatch metric name ElastiCacheDatabaseMemoryUs DatabaseMemoryUsagePercentage agePercentage Amazon Keyspaces CassandraReadCapacityUtilization CassandraWriteCapacityUtili zation Lambda ProvisionedReadCapacityUnits, ConsumedR eadCapacityUnits² ProvisionedWriteCapacityUnits, ConsumedW riteCapacityUnits² LambdaProvisionedConcurrenc ProvisionedConcurrencyUtilization yUtilization Amazon MSK KafkaBrokerStorageUtilization KafkaDataLogsDiskUsed Neptune NeptuneReaderAverageCPUUtil CPUUtilization ization SageMaker AI SageMakerVariantInvocations InvocationsPerInstance¹ PerInstance SageMakerInferenceComponent InvocationsPerCopy¹ InvocationsPerCopy SageMakerVariantProvisioned ServerlessProvisionedConcurrencyUtilization ConcurrencyUtilization Predeﬁned metrics for target tracking scaling policies 155 Application Auto Scaling User Guide Predeﬁned metric type CloudWatch metric name SageMakerInferenceComponent ConcurrentRequestsPerCopy ConcurrentRequestsPerCopyHi ghResolution SageMakerVariantConcurrentR ConcurrentRequestsPerModel equestsPerModelHighResolution Spot Fleet EC2SpotFleetRequestAverageC CPUUtilization³ PUUtilization EC2SpotFleetRequestAverageN NetworkIn¹ ³ etworkIn³ EC2SpotFleetRequestAverageN NetworkOut¹ ³ etworkOut³ ALBRequestCountPerTarget RequestCountPerTarget¹ ¹ Metric is based on a count instead of a percentage. ² For DynamoDB and Amazon Keyspaces, the predeﬁned metrics are an aggregation of two CloudWatch metrics to support scaling based on provisioned throughput consumption. ³ For best scaling performance, Amazon EC2 detailed monitoring should be used. Log Application Auto Scaling API calls using AWS CloudTrail Application Auto Scaling is integrated with AWS CloudTrail, a service that provides a record of actions taken by a user, role, or an AWS service. CloudTrail captures API calls for Application Auto Scaling as events. The calls captured include calls from the AWS Management Console and code calls to the Application Auto Scaling API operations. Using the information collected by CloudTrail, you can determine the request that was made to Application Auto Scaling, the IP address from which the request was made, when it was made, and additional details. Log API calls using CloudTrail 156 Application Auto Scaling User Guide Every event or log entry contains information about who generated the request. The identity information helps you determine the following: • Whether the request was made with root user or user credentials. • Whether the request was made on behalf of an IAM Identity Center user. • Whether the request was made with temporary security credentials
app-as-ug-047	app-as-ug.pdf	47	the information collected by CloudTrail, you can determine the request that was made to Application Auto Scaling, the IP address from which the request was made, when it was made, and additional details. Log API calls using CloudTrail 156 Application Auto Scaling User Guide Every event or log entry contains information about who generated the request. The identity information helps you determine the following: • Whether the request was made with root user or user credentials. • Whether the request was made on behalf of an IAM Identity Center user. • Whether the request was made with temporary security credentials for a role or federated user. • Whether the request was made by another AWS service. CloudTrail is active in your AWS account when you create the account and you automatically have access to the CloudTrail Event history. The CloudTrail Event history provides a viewable, searchable, downloadable, and immutable record of the past 90 days of recorded management events in an AWS Region. For more information, see Working with CloudTrail Event history in the AWS CloudTrail User Guide. There are no CloudTrail charges for viewing the Event history. For an ongoing record of events in your AWS account past 90 days, create a trail. CloudTrail trails A trail enables CloudTrail to deliver log ﬁles to an Amazon S3 bucket. All trails created using the AWS Management Console are multi-Region. You can create a single-Region or a multi-Region trail by using the AWS CLI. Creating a multi-Region trail is recommended because you capture activity in all AWS Regions in your account. If you create a single-Region trail, you can view only the events logged in the trail's AWS Region. For more information about trails, see Creating a trail for your AWS account and Creating a trail for an organization in the AWS CloudTrail User Guide. You can deliver one copy of your ongoing management events to your Amazon S3 bucket at no charge from CloudTrail by creating a trail, however, there are Amazon S3 storage charges. For more information about CloudTrail pricing, see AWS CloudTrail Pricing. For information about Amazon S3 pricing, see Amazon S3 Pricing. Application Auto Scaling management events in CloudTrail Management events provide information about management operations that are performed on resources in your AWS account. These are also known as control plane operations. By default, CloudTrail logs management events. Application Auto Scaling management events in CloudTrail 157 Application Auto Scaling User Guide Application Auto Scaling logs all Application Auto Scaling control plane operations as management events. For a list of the Application Auto Scaling control plane operations that Application Auto Scaling logs to CloudTrail, see the Application Auto Scaling API Reference. Application Auto Scaling event examples An event represents a single request from any source and includes information about the requested API operation, the date and time of the operation, request parameters, and so on. CloudTrail log ﬁles aren't an ordered stack trace of the public API calls, so events don't appear in any speciﬁc order. The following example shows a CloudTrail event that demonstrates the DescribeScalableTargets operation. { "eventVersion": "1.05", "userIdentity": { "type": "Root", "principalId": "123456789012", "arn": "arn:aws:iam::123456789012:root", "accountId": "123456789012", "accessKeyId": "AKIAIOSFODNN7EXAMPLE", "sessionContext": { "attributes": { "mfaAuthenticated": "false", "creationDate": "2018-08-21T17:05:42Z" } } }, "eventTime": "2018-08-16T23:20:32Z", "eventSource": "autoscaling.amazonaws.com", "eventName": "DescribeScalableTargets", "awsRegion": "us-west-2", "sourceIPAddress": "72.21.196.68", "userAgent": "EC2 Spot Console", "requestParameters": { "serviceNamespace": "ec2", "scalableDimension": "ec2:spot-fleet-request:TargetCapacity", "resourceIds": [ "spot-fleet-request/sfr-05ceaf79-3ba2-405d-e87b-612857f1357a" ] }, Application Auto Scaling event examples 158 Application Auto Scaling User Guide "responseElements": null, "additionalEventData": { "service": "application-autoscaling" }, "requestID": "0737e2ea-fb2d-11e3-bfd8-99133058e7bb", "eventID": "3fcfb182-98f8-4744-bd45-b38835ab61cb", "eventType": "AwsApiCall", "recipientAccountId": "123456789012" } For information about CloudTrail record contents, see CloudTrail record contents in the AWS CloudTrail User Guide. Application Auto Scaling RemoveAction calls on CloudWatch Your AWS CloudTrail log might show that Application Auto Scaling calls the CloudWatch RemoveAction API when Application Auto Scaling instructs CloudWatch to remove the automatic scaling action from an alarm. This could happen if you deregister a scalable target, delete a scaling policy, or if an alarm invokes a nonexistent scaling policy. Monitor Application Auto Scaling events using Amazon EventBridge Amazon EventBridge, formerly called CloudWatch Events, helps you monitor events that are speciﬁc to Application Auto Scaling and initiate target actions that use other AWS services. Events from AWS services are delivered to EventBridge in near real time. Using EventBridge, you can create rules that match incoming events and route them to targets for processing. For more information, see Getting started with Amazon EventBridge in the Amazon EventBridge User Guide. Application Auto Scaling events The following examples show events for Application Auto Scaling. Events are produced on a best- eﬀort basis. Only events that are speciﬁc to scaled to max and API calls via CloudTrail are currently available for Application Auto Scaling. Application Auto Scaling RemoveAction calls on CloudWatch 159 Application Auto Scaling Event types • Event for
app-as-ug-048	app-as-ug.pdf	48	AWS services are delivered to EventBridge in near real time. Using EventBridge, you can create rules that match incoming events and route them to targets for processing. For more information, see Getting started with Amazon EventBridge in the Amazon EventBridge User Guide. Application Auto Scaling events The following examples show events for Application Auto Scaling. Events are produced on a best- eﬀort basis. Only events that are speciﬁc to scaled to max and API calls via CloudTrail are currently available for Application Auto Scaling. Application Auto Scaling RemoveAction calls on CloudWatch 159 Application Auto Scaling Event types • Event for state change: scaled to max • Events for API calls via CloudTrail Event for state change: scaled to max User Guide The following example event shows that Application Auto Scaling increased (scaled out) the capacity of the scalable target to its maximum size limit. If demand increases again, Application Auto Scaling will be prevented from scaling the target to a larger size because it is already scaled to its maximum size. In the detail object, the values for the resourceId, serviceNamespace, and scalableDimension attributes identify the scalable target. The values for the newDesiredCapacity and oldDesiredCapacity attributes refer to the new capacity after the scale-out event and the original capacity before the scale-out event. The maxCapacity is the maximum size limit of the scalable target. { "version": "0", "id": "11112222-3333-4444-5555-666677778888", "detail-type": "Application Auto Scaling Scaling Activity State Change", "source": "aws.application-autoscaling", "account": "123456789012", "time": "2019-06-12T10:23:40Z", "region": "us-west-2", "resources": [], "detail": { "startTime": "2022-06-12T10:20:43Z", "endTime": "2022-06-12T10:23:40Z", "newDesiredCapacity": 8, "oldDesiredCapacity": 5, "minCapacity": 2, "maxCapacity": 8, "resourceId": "table/my-table", "scalableDimension": "dynamodb:table:WriteCapacityUnits", "serviceNamespace": "dynamodb", "statusCode": "Successful", "scaledToMax": true, "direction": "scale-out" } Application Auto Scaling events 160 Application Auto Scaling User Guide To create a rule that captures all scaledToMax state change events for all scalable targets, use the following sample event pattern. { "source": [ "aws.application-autoscaling" ], "detail-type": [ "Application Auto Scaling Scaling Activity State Change" ], "detail": { "scaledToMax": [ true ] } } Events for API calls via CloudTrail A trail is a conﬁguration that AWS CloudTrail uses to deliver events as log ﬁles to an Amazon S3 bucket. CloudTrail log ﬁles contain log entries. An event represents a log entry, and it includes information about the requested action, the date and time of the action, and request parameters. To learn how to get started with CloudTrail, see Creating a trail in the AWS CloudTrail User Guide. Events that are delivered via CloudTrail have AWS API Call via CloudTrail as the value for detail-type. The following example event represents a CloudTrail log ﬁle entry that shows that a console user called the Application Auto Scaling RegisterScalableTarget action. { "version": "0", "id": "99998888-7777-6666-5555-444433332222", "detail-type": "AWS API Call via CloudTrail", "source": "aws.autoscaling", "account": "123456789012", "time": "2022-07-13T16:50:15Z", "region": "us-west-2", "resources": [], "detail": { "eventVersion": "1.08", "userIdentity": { Application Auto Scaling events 161 User Guide Application Auto Scaling "type": "IAMUser", "principalId": "123456789012", "arn": "arn:aws:iam::123456789012:user/Bob", "accountId": "123456789012", "accessKeyId": "AKIAIOSFODNN7EXAMPLE", "sessionContext": { "sessionIssuer": { "type": "Role", "principalId": "123456789012", "arn": "arn:aws:iam::123456789012:role/Admin", "accountId": "123456789012", "userName": "Admin" }, "webIdFederationData": {}, "attributes": { "creationDate": "2022-07-13T15:17:08Z", "mfaAuthenticated": "false" } } }, "eventTime": "2022-07-13T16:50:15Z", "eventSource": "autoscaling.amazonaws.com", "eventName": "RegisterScalableTarget", "awsRegion": "us-west-2", "sourceIPAddress": "AWS Internal", "userAgent": "EC2 Spot Console", "requestParameters": { "resourceId": "spot-fleet-request/sfr-73fbd2ce-aa30-494c-8788-1cee4EXAMPLE", "serviceNamespace": "ec2", "scalableDimension": "ec2:spot-fleet-request:TargetCapacity", "minCapacity": 2, "maxCapacity": 10 }, "responseElements": null, "additionalEventData": { "service": "application-autoscaling" }, "requestID": "e9caf887-8d88-11e5-a331-3332aa445952", "eventID": "49d14f36-6450-44a5-a501-b0fdcdfaeb98", "readOnly": false, "eventType": "AwsApiCall", "managementEvent": true, "recipientAccountId": "123456789012", "eventCategory": "Management", Application Auto Scaling events 162 Application Auto Scaling User Guide "sessionCredentialFromConsole": "true" } } To create a rule based on all DeleteScalingPolicy and DeregisterScalableTarget API calls for all scalable targets, use the following sample event pattern: { "source": [ "aws.autoscaling" ], "detail-type": [ "AWS API Call via CloudTrail" ], "detail": { "eventSource": [ "autoscaling.amazonaws.com" ], "eventName": [ "DeleteScalingPolicy", "DeregisterScalableTarget" ], "additionalEventData": { "service": [ "application-autoscaling" ] } } } For more information about using CloudTrail, see Log Application Auto Scaling API calls using AWS CloudTrail. Application Auto Scaling events 163 Application Auto Scaling User Guide Using this service with an AWS SDK AWS software development kits (SDKs) are available for many popular programming languages. Each SDK provides an API, code examples, and documentation that make it easier for developers to build applications in their preferred language. SDK documentation Code examples AWS SDK for C++ AWS SDK for C++ code examples AWS CLI AWS SDK for Go AWS SDK for Java AWS CLI code examples AWS SDK for Go code examples AWS SDK for Java code examples AWS SDK for JavaScript AWS SDK for JavaScript code examples AWS SDK for Kotlin AWS SDK for Kotlin code examples AWS SDK for .NET AWS SDK for PHP AWS SDK for .NET code examples AWS SDK for PHP code examples AWS Tools for PowerShell Tools for PowerShell code examples AWS SDK for Python (Boto3) AWS SDK for Python (Boto3) code
app-as-ug-049	app-as-ug.pdf	49	SDK documentation Code examples AWS SDK for C++ AWS SDK for C++ code examples AWS CLI AWS SDK for Go AWS SDK for Java AWS CLI code examples AWS SDK for Go code examples AWS SDK for Java code examples AWS SDK for JavaScript AWS SDK for JavaScript code examples AWS SDK for Kotlin AWS SDK for Kotlin code examples AWS SDK for .NET AWS SDK for PHP AWS SDK for .NET code examples AWS SDK for PHP code examples AWS Tools for PowerShell Tools for PowerShell code examples AWS SDK for Python (Boto3) AWS SDK for Python (Boto3) code examples AWS SDK for Ruby AWS SDK for Rust AWS SDK for Ruby code examples AWS SDK for Rust code examples AWS SDK for SAP ABAP AWS SDK for SAP ABAP code examples AWS SDK for Swift AWS SDK for Swift code examples For examples speciﬁc to this service, see Code examples for Application Auto Scaling using AWS SDKs. 164 Application Auto Scaling User Guide Example availability Can't ﬁnd what you need? Request a code example by using the Provide feedback link at the bottom of this page. 165 Application Auto Scaling User Guide Code examples for Application Auto Scaling using AWS SDKs The following code examples show how to use Application Auto Scaling with an AWS software development kit (SDK). Actions are code excerpts from larger programs and must be run in context. While actions show you how to call individual service functions, you can see actions in context in their related scenarios. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Code examples • Basic examples for Application Auto Scaling using AWS SDKs • Actions for Application Auto Scaling using AWS SDKs • Use DeleteScalingPolicy with an AWS SDK or CLI • Use DeleteScheduledAction with a CLI • Use DeregisterScalableTarget with a CLI • Use DescribeScalableTargets with a CLI • Use DescribeScalingActivities with a CLI • Use DescribeScalingPolicies with an AWS SDK or CLI • Use DescribeScheduledActions with a CLI • Use PutScalingPolicy with a CLI • Use PutScheduledAction with a CLI • Use RegisterScalableTarget with an AWS SDK or CLI Basic examples for Application Auto Scaling using AWS SDKs The following code examples show how to use the basics of Application Auto Scaling with AWS SDKs. Examples • Actions for Application Auto Scaling using AWS SDKs Basics 166 Application Auto Scaling User Guide • Use DeleteScalingPolicy with an AWS SDK or CLI • Use DeleteScheduledAction with a CLI • Use DeregisterScalableTarget with a CLI • Use DescribeScalableTargets with a CLI • Use DescribeScalingActivities with a CLI • Use DescribeScalingPolicies with an AWS SDK or CLI • Use DescribeScheduledActions with a CLI • Use PutScalingPolicy with a CLI • Use PutScheduledAction with a CLI • Use RegisterScalableTarget with an AWS SDK or CLI Actions for Application Auto Scaling using AWS SDKs The following code examples demonstrate how to perform individual Application Auto Scaling actions with AWS SDKs. Each example includes a link to GitHub, where you can ﬁnd instructions for setting up and running the code. The following examples include only the most commonly used actions. For a complete list, see the Application Auto Scaling API Reference. Examples • Use DeleteScalingPolicy with an AWS SDK or CLI • Use DeleteScheduledAction with a CLI • Use DeregisterScalableTarget with a CLI • Use DescribeScalableTargets with a CLI • Use DescribeScalingActivities with a CLI • Use DescribeScalingPolicies with an AWS SDK or CLI • Use DescribeScheduledActions with a CLI • Use PutScalingPolicy with a CLI • Use PutScheduledAction with a CLI • Use RegisterScalableTarget with an AWS SDK or CLI Actions 167 Application Auto Scaling User Guide Use DeleteScalingPolicy with an AWS SDK or CLI The following code examples show how to use DeleteScalingPolicy. CLI AWS CLI To delete a scaling policy This example deletes a scaling policy for the Amazon ECS service web-app running in the default cluster. Command: aws application-autoscaling delete-scaling-policy --policy-name web-app-cpu-lt-25 --scalable-dimension ecs:service:DesiredCount --resource-id service/default/web- app --service-namespace ecs • For API details, see DeleteScalingPolicy in AWS CLI Command Reference. Java SDK for Java 2.x Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.applicationautoscaling.ApplicationAutoScalingClient; import software.amazon.awssdk.services.applicationautoscaling.model.ApplicationAutoScalingException; import software.amazon.awssdk.services.applicationautoscaling.model.DeleteScalingPolicyRequest; import software.amazon.awssdk.services.applicationautoscaling.model.DeregisterScalableTargetRequest; Actions 168 Application Auto Scaling import User Guide software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalableTargetsRequest; import software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalableTargetsResponse; import software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalingPoliciesRequest; import software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalingPoliciesResponse; import software.amazon.awssdk.services.applicationautoscaling.model.ScalableDimension; import software.amazon.awssdk.services.applicationautoscaling.model.ServiceNamespace; /** * Before running this Java V2 code example, set up your development environment, including your credentials. * * For more information, see the following documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get- started.html */ public class DisableDynamoDBAutoscaling { public static void main(String[] args) { final String usage =
app-as-ug-050	app-as-ug.pdf	50	Java 2.x Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.applicationautoscaling.ApplicationAutoScalingClient; import software.amazon.awssdk.services.applicationautoscaling.model.ApplicationAutoScalingException; import software.amazon.awssdk.services.applicationautoscaling.model.DeleteScalingPolicyRequest; import software.amazon.awssdk.services.applicationautoscaling.model.DeregisterScalableTargetRequest; Actions 168 Application Auto Scaling import User Guide software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalableTargetsRequest; import software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalableTargetsResponse; import software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalingPoliciesRequest; import software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalingPoliciesResponse; import software.amazon.awssdk.services.applicationautoscaling.model.ScalableDimension; import software.amazon.awssdk.services.applicationautoscaling.model.ServiceNamespace; /** * Before running this Java V2 code example, set up your development environment, including your credentials. * * For more information, see the following documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get- started.html */ public class DisableDynamoDBAutoscaling { public static void main(String[] args) { final String usage = """ Usage: <tableId> <policyName>\s Where: tableId - The table Id value (for example, table/Music).\s policyName - The name of the policy (for example, $Music5-scaling- policy). """; if (args.length != 2) { System.out.println(usage); System.exit(1); } ApplicationAutoScalingClient appAutoScalingClient = ApplicationAutoScalingClient.builder() .region(Region.US_EAST_1) Actions 169 Application Auto Scaling User Guide .build(); ServiceNamespace ns = ServiceNamespace.DYNAMODB; ScalableDimension tableWCUs = ScalableDimension.DYNAMODB_TABLE_WRITE_CAPACITY_UNITS; String tableId = args[0]; String policyName = args[1]; deletePolicy(appAutoScalingClient, policyName, tableWCUs, ns, tableId); verifyScalingPolicies(appAutoScalingClient, tableId, ns, tableWCUs); deregisterScalableTarget(appAutoScalingClient, tableId, ns, tableWCUs); verifyTarget(appAutoScalingClient, tableId, ns, tableWCUs); } public static void deletePolicy(ApplicationAutoScalingClient appAutoScalingClient, String policyName, ScalableDimension tableWCUs, ServiceNamespace ns, String tableId) { try { DeleteScalingPolicyRequest delSPRequest = DeleteScalingPolicyRequest.builder() .policyName(policyName) .scalableDimension(tableWCUs) .serviceNamespace(ns) .resourceId(tableId) .build(); appAutoScalingClient.deleteScalingPolicy(delSPRequest); System.out.println(policyName +" was deleted successfully."); } catch (ApplicationAutoScalingException e) { System.err.println(e.awsErrorDetails().errorMessage()); } } // Verify that the scaling policy was deleted public static void verifyScalingPolicies(ApplicationAutoScalingClient appAutoScalingClient, String tableId, ServiceNamespace ns, ScalableDimension tableWCUs) { DescribeScalingPoliciesRequest dscRequest = DescribeScalingPoliciesRequest.builder() .scalableDimension(tableWCUs) .serviceNamespace(ns) .resourceId(tableId) .build(); Actions 170 Application Auto Scaling User Guide DescribeScalingPoliciesResponse response = appAutoScalingClient.describeScalingPolicies(dscRequest); System.out.println("DescribeScalableTargets result: "); System.out.println(response); } public static void deregisterScalableTarget(ApplicationAutoScalingClient appAutoScalingClient, String tableId, ServiceNamespace ns, ScalableDimension tableWCUs) { try { DeregisterScalableTargetRequest targetRequest = DeregisterScalableTargetRequest.builder() .scalableDimension(tableWCUs) .serviceNamespace(ns) .resourceId(tableId) .build(); appAutoScalingClient.deregisterScalableTarget(targetRequest); System.out.println("The scalable target was deregistered."); } catch (ApplicationAutoScalingException e) { System.err.println(e.awsErrorDetails().errorMessage()); } } public static void verifyTarget(ApplicationAutoScalingClient appAutoScalingClient, String tableId, ServiceNamespace ns, ScalableDimension tableWCUs) { DescribeScalableTargetsRequest dscRequest = DescribeScalableTargetsRequest.builder() .scalableDimension(tableWCUs) .serviceNamespace(ns) .resourceIds(tableId) .build(); DescribeScalableTargetsResponse response = appAutoScalingClient.describeScalableTargets(dscRequest); System.out.println("DescribeScalableTargets result: "); System.out.println(response); } } Actions 171 Application Auto Scaling User Guide • For API details, see DeleteScalingPolicy in AWS SDK for Java 2.x API Reference. PowerShell Tools for PowerShell Example 1: This cmdlet deletes the speciﬁed scaling policy for an Application Auto Scaling scalable target. Remove-AASScalingPolicy -ServiceNamespace AppStream -PolicyName "default-scale- out" -ResourceId fleet/Test -ScalableDimension appstream:fleet:DesiredCapacity • For API details, see DeleteScalingPolicy in AWS Tools for PowerShell Cmdlet Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use DeleteScheduledAction with a CLI The following code examples show how to use DeleteScheduledAction. CLI AWS CLI To delete a scheduled action The follwing delete-scheduled-action example deletes the speciﬁed scheduled action from the speciﬁed Amazon AppStream 2.0 ﬂeet: aws application-autoscaling delete-scheduled-action \ --service-namespace appstream \ --scalable-dimension appstream:fleet:DesiredCapacity \ --resource-id fleet/sample-fleet \ --scheduled-action-name my-recurring-action This command produces no output. For more information, see Scheduled Scaling in the Application Auto Scaling User Guide. Actions 172 Application Auto Scaling User Guide • For API details, see DeleteScheduledAction in AWS CLI Command Reference. PowerShell Tools for PowerShell Example 1: This cmdlet deletes the speciﬁed scheduled action for an Application Auto Scaling scalable target. Remove-AASScheduledAction -ServiceNamespace AppStream -ScheduledActionName WeekDaysFleetScaling -ResourceId fleet/MyFleet -ScalableDimension appstream:fleet:DesiredCapacity Output: Confirm Are you sure you want to perform this action? Performing the operation "Remove-AASScheduledAction (DeleteScheduledAction)" on target "WeekDaysFleetScaling". [Y] Yes [A] Yes to All [N] No [L] No to All [S] Suspend [?] Help (default is "Y"): Y • For API details, see DeleteScheduledAction in AWS Tools for PowerShell Cmdlet Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use DeregisterScalableTarget with a CLI The following code examples show how to use DeregisterScalableTarget. CLI AWS CLI To deregister a scalable target This example deregisters a scalable target for an Amazon ECS service called web-app that is running in the default cluster. Actions 173 Application Auto Scaling Command: User Guide aws application-autoscaling deregister-scalable-target --service-namespace ecs -- scalable-dimension ecs:service:DesiredCount --resource-id service/default/web-app This example deregisters a scalable target for a custom resource. The custom-resource-id.txt ﬁle contains a string that identiﬁes the Resource ID, which, for a custom resource, is the path to the custom resource through your Amazon API Gateway endpoint. Command: aws application-autoscaling deregister-scalable-target -- service-namespace custom-resource --scalable-dimension custom- resource:ResourceType:Property --resource-id file://~/custom-resource-id.txt Contents of custom-resource-id.txt ﬁle: https://example.execute-api.us-west-2.amazonaws.com/prod/ scalableTargetDimensions/1-23456789 • For API details, see DeregisterScalableTarget in AWS CLI Command Reference. PowerShell Tools for PowerShell Example 1: This cmdlet deregisters an Application Auto Scaling scalable target.Deregistering a scalable target deletes the scaling policies that are associated with it. Remove-AASScalableTarget -ResourceId fleet/MyFleet -ScalableDimension appstream:fleet:DesiredCapacity -ServiceNamespace AppStream Output: Confirm Are you sure you
app-as-ug-051	app-as-ug.pdf	51	custom resource. The custom-resource-id.txt ﬁle contains a string that identiﬁes the Resource ID, which, for a custom resource, is the path to the custom resource through your Amazon API Gateway endpoint. Command: aws application-autoscaling deregister-scalable-target -- service-namespace custom-resource --scalable-dimension custom- resource:ResourceType:Property --resource-id file://~/custom-resource-id.txt Contents of custom-resource-id.txt ﬁle: https://example.execute-api.us-west-2.amazonaws.com/prod/ scalableTargetDimensions/1-23456789 • For API details, see DeregisterScalableTarget in AWS CLI Command Reference. PowerShell Tools for PowerShell Example 1: This cmdlet deregisters an Application Auto Scaling scalable target.Deregistering a scalable target deletes the scaling policies that are associated with it. Remove-AASScalableTarget -ResourceId fleet/MyFleet -ScalableDimension appstream:fleet:DesiredCapacity -ServiceNamespace AppStream Output: Confirm Are you sure you want to perform this action? Performing the operation "Remove-AASScalableTarget (DeregisterScalableTarget)" on target "fleet/MyFleet". Actions 174 Application Auto Scaling User Guide [Y] Yes [A] Yes to All [N] No [L] No to All [S] Suspend [?] Help (default is "Y"): Y • For API details, see DeregisterScalableTarget in AWS Tools for PowerShell Cmdlet Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use DescribeScalableTargets with a CLI The following code examples show how to use DescribeScalableTargets. CLI AWS CLI To describe scalable targets The following describe-scalable-targets example describes the scalable targets for the ecs service namespace. aws application-autoscaling describe-scalable-targets \ --service-namespace ecs Output: { "ScalableTargets": [ { "ServiceNamespace": "ecs", "ScalableDimension": "ecs:service:DesiredCount", "ResourceId": "service/default/web-app", "MinCapacity": 1, "MaxCapacity": 10, "RoleARN": "arn:aws:iam::123456789012:role/ aws-service-role/ecs.application-autoscaling.amazonaws.com/ AWSServiceRoleForApplicationAutoScaling_ECSService", "CreationTime": 1462558906.199, "SuspendedState": { "DynamicScalingOutSuspended": false, Actions 175 Application Auto Scaling User Guide "ScheduledScalingSuspended": false, "DynamicScalingInSuspended": false }, "ScalableTargetARN": "arn:aws:application-autoscaling:us- west-2:123456789012:scalable-target/1234abcd56ab78cd901ef1234567890ab123" } ] } For more information, see AWS services that you can use with Application Auto Scaling in the Application Auto Scaling User Guide. • For API details, see DescribeScalableTargets in AWS CLI Command Reference. PowerShell Tools for PowerShell Example 1: This example will provide information about the Application Autoscaling Scalable targets in the speciﬁed namespace. Get-AASScalableTarget -ServiceNamespace "AppStream" Output: CreationTime : 11/7/2019 2:30:03 AM MaxCapacity : 5 MinCapacity : 1 ResourceId : fleet/Test RoleARN : arn:aws:iam::012345678912:role/aws- service-role/appstream.application-autoscaling.amazonaws.com/ AWSServiceRoleForApplicationAutoScaling_AppStreamFleet ScalableDimension : appstream:fleet:DesiredCapacity ServiceNamespace : appstream SuspendedState : Amazon.ApplicationAutoScaling.Model.SuspendedState • For API details, see DescribeScalableTargets in AWS Tools for PowerShell Cmdlet Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Actions 176 Application Auto Scaling User Guide Use DescribeScalingActivities with a CLI The following code examples show how to use DescribeScalingActivities. CLI AWS CLI Example 1: To describe scaling activities for the speciﬁed Amazon ECS service The following describe-scaling-activities example describes the scaling activities for an Amazon ECS service called web-app that is running in the default cluster. The output shows a scaling activity initiated by a scaling policy. aws application-autoscaling describe-scaling-activities \ --service-namespace ecs \ --resource-id service/default/web-app Output: { "ScalingActivities": [ { "ScalableDimension": "ecs:service:DesiredCount", "Description": "Setting desired count to 1.", "ResourceId": "service/default/web-app", "ActivityId": "e6c5f7d1-dbbb-4a3f-89b2-51f33e766399", "StartTime": 1462575838.171, "ServiceNamespace": "ecs", "EndTime": 1462575872.111, "Cause": "monitor alarm web-app-cpu-lt-25 in state ALARM triggered policy web-app-cpu-lt-25", "StatusMessage": "Successfully set desired count to 1. Change successfully fulfilled by ecs.", "StatusCode": "Successful" } ] } For more information, see Scaling activities for Application Auto Scaling in the Application Auto Scaling User Guide. Actions 177 Application Auto Scaling User Guide Example 2: To describe scaling activities for the speciﬁed DynamoDB table The following describe-scaling-activities example describes the scaling activities for a DynamoDB table called TestTable. The output shows scaling activities initiated by two diﬀerent scheduled actions. aws application-autoscaling describe-scaling-activities \ --service-namespace dynamodb \ --resource-id table/TestTable Output: { "ScalingActivities": [ { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting write capacity units to 10.", "ResourceId": "table/my-table", "ActivityId": "4d1308c0-bbcf-4514-a673-b0220ae38547", "StartTime": 1561574415.086, "ServiceNamespace": "dynamodb", "EndTime": 1561574449.51, "Cause": "maximum capacity was set to 10", "StatusMessage": "Successfully set write capacity units to 10. Change successfully fulfilled by dynamodb.", "StatusCode": "Successful" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting min capacity to 5 and max capacity to 10", "ResourceId": "table/my-table", "ActivityId": "f2b7847b-721d-4e01-8ef0-0c8d3bacc1c7", "StartTime": 1561574414.644, "ServiceNamespace": "dynamodb", "Cause": "scheduled action name my-second-scheduled-action was triggered", "StatusMessage": "Successfully set min capacity to 5 and max capacity to 10", "StatusCode": "Successful" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting write capacity units to 15.", Actions 178 Application Auto Scaling User Guide "ResourceId": "table/my-table", "ActivityId": "d8ea4de6-9eaa-499f-b466-2cc5e681ba8b", "StartTime": 1561574108.904, "ServiceNamespace": "dynamodb", "EndTime": 1561574140.255, "Cause": "minimum capacity was set to 15", "StatusMessage": "Successfully set write capacity units to 15. Change successfully fulfilled by dynamodb.", "StatusCode": "Successful" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting min capacity to 15 and max capacity to 20", "ResourceId": "table/my-table", "ActivityId": "3250fd06-6940-4e8e-bb1f-d494db7554d2", "StartTime": 1561574108.512, "ServiceNamespace": "dynamodb", "Cause": "scheduled action name my-first-scheduled-action was triggered", "StatusMessage": "Successfully set min capacity to 15 and max capacity to 20", "StatusCode": "Successful" } ] } For more information, see Scaling activities for Application Auto Scaling in the Application Auto
app-as-ug-052	app-as-ug.pdf	52	Actions 178 Application Auto Scaling User Guide "ResourceId": "table/my-table", "ActivityId": "d8ea4de6-9eaa-499f-b466-2cc5e681ba8b", "StartTime": 1561574108.904, "ServiceNamespace": "dynamodb", "EndTime": 1561574140.255, "Cause": "minimum capacity was set to 15", "StatusMessage": "Successfully set write capacity units to 15. Change successfully fulfilled by dynamodb.", "StatusCode": "Successful" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Description": "Setting min capacity to 15 and max capacity to 20", "ResourceId": "table/my-table", "ActivityId": "3250fd06-6940-4e8e-bb1f-d494db7554d2", "StartTime": 1561574108.512, "ServiceNamespace": "dynamodb", "Cause": "scheduled action name my-first-scheduled-action was triggered", "StatusMessage": "Successfully set min capacity to 15 and max capacity to 20", "StatusCode": "Successful" } ] } For more information, see Scaling activities for Application Auto Scaling in the Application Auto Scaling User Guide. • For API details, see DescribeScalingActivities in AWS CLI Command Reference. PowerShell Tools for PowerShell Example 1: Provides descriptive information about the scaling activities in the speciﬁed namespace from the previous six weeks. Get-AASScalingActivity -ServiceNamespace AppStream Output: Actions 179 Application Auto Scaling User Guide ActivityId : 2827409f-b639-4cdb-a957-8055d5d07434 Cause : monitor alarm Appstream2-MyFleet-default-scale-in-Alarm in state ALARM triggered policy default-scale-in Description : Setting desired capacity to 2. Details : EndTime : 12/14/2019 11:32:49 AM ResourceId : fleet/MyFleet ScalableDimension : appstream:fleet:DesiredCapacity ServiceNamespace : appstream StartTime : 12/14/2019 11:32:14 AM StatusCode : Successful StatusMessage : Successfully set desired capacity to 2. Change successfully fulfilled by appstream. • For API details, see DescribeScalingActivities in AWS Tools for PowerShell Cmdlet Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use DescribeScalingPolicies with an AWS SDK or CLI The following code examples show how to use DescribeScalingPolicies. CLI AWS CLI To describe scaling policies This example command describes the scaling policies for the ecs service namespace. Command: aws application-autoscaling describe-scaling-policies --service-namespace ecs Output: { "ScalingPolicies": [ { "PolicyName": "web-app-cpu-gt-75", Actions 180 Application Auto Scaling User Guide "ScalableDimension": "ecs:service:DesiredCount", "ResourceId": "service/default/web-app", "CreationTime": 1462561899.23, "StepScalingPolicyConfiguration": { "Cooldown": 60, "StepAdjustments": [ { "ScalingAdjustment": 200, "MetricIntervalLowerBound": 0.0 } ], "AdjustmentType": "PercentChangeInCapacity" }, "PolicyARN": "arn:aws:autoscaling:us- west-2:012345678910:scalingPolicy:6d8972f3-efc8-437c-92d1-6270f29a66e7:resource/ ecs/service/default/web-app:policyName/web-app-cpu-gt-75", "PolicyType": "StepScaling", "Alarms": [ { "AlarmName": "web-app-cpu-gt-75", "AlarmARN": "arn:aws:cloudwatch:us- west-2:012345678910:alarm:web-app-cpu-gt-75" } ], "ServiceNamespace": "ecs" }, { "PolicyName": "web-app-cpu-lt-25", "ScalableDimension": "ecs:service:DesiredCount", "ResourceId": "service/default/web-app", "CreationTime": 1462562575.099, "StepScalingPolicyConfiguration": { "Cooldown": 1, "StepAdjustments": [ { "ScalingAdjustment": -50, "MetricIntervalUpperBound": 0.0 } ], "AdjustmentType": "PercentChangeInCapacity" }, "PolicyARN": "arn:aws:autoscaling:us- west-2:012345678910:scalingPolicy:6d8972f3-efc8-437c-92d1-6270f29a66e7:resource/ ecs/service/default/web-app:policyName/web-app-cpu-lt-25", Actions 181 Application Auto Scaling User Guide "PolicyType": "StepScaling", "Alarms": [ { "AlarmName": "web-app-cpu-lt-25", "AlarmARN": "arn:aws:cloudwatch:us- west-2:012345678910:alarm:web-app-cpu-lt-25" } ], "ServiceNamespace": "ecs" } ] } • For API details, see DescribeScalingPolicies in AWS CLI Command Reference. PowerShell Tools for PowerShell Example 1: This cmdlet describe the Application Auto Scaling scaling policies for the speciﬁed service namespace. Get-AASScalingPolicy -ServiceNamespace AppStream Output: Alarms : {Appstream2-LabFleet-default-scale- out-Alarm} CreationTime : 9/3/2019 2:48:15 AM PolicyARN : arn:aws:autoscaling:us- west-2:012345678912:scalingPolicy:5659b069-b5cd-4af1-9f7f-3e956d36233e:resource/ appstream/fleet/LabFleet: policyName/default-scale-out PolicyName : default-scale-out PolicyType : StepScaling ResourceId : fleet/LabFleet ScalableDimension : appstream:fleet:DesiredCapacity ServiceNamespace : appstream StepScalingPolicyConfiguration : Amazon.ApplicationAutoScaling.Model.StepScalingPolicyConfiguration TargetTrackingScalingPolicyConfiguration : Actions 182 Application Auto Scaling User Guide Alarms : {Appstream2-LabFleet-default-scale-in- Alarm} CreationTime : 9/3/2019 2:48:15 AM PolicyARN : arn:aws:autoscaling:us- west-2:012345678912:scalingPolicy:5659b069-b5cd-4af1-9f7f-3e956d36233e:resource/ appstream/fleet/LabFleet: policyName/default-scale-in PolicyName : default-scale-in PolicyType : StepScaling ResourceId : fleet/LabFleet ScalableDimension : appstream:fleet:DesiredCapacity ServiceNamespace : appstream StepScalingPolicyConfiguration : Amazon.ApplicationAutoScaling.Model.StepScalingPolicyConfiguration TargetTrackingScalingPolicyConfiguration : • For API details, see DescribeScalingPolicies in AWS Tools for PowerShell Cmdlet Reference. Rust SDK for Rust Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. async fn show_policies(client: &Client) -> Result<(), Error> { let response = client .describe_scaling_policies() .service_namespace(ServiceNamespace::Ec2) .send() .await?; println!("Auto Scaling Policies:"); for policy in response.scaling_policies() { println!("{:?}\n", policy); } println!("Next token: {:?}", response.next_token()); Ok(()) } Actions 183 Application Auto Scaling User Guide • For API details, see DescribeScalingPolicies in AWS SDK for Rust API reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use DescribeScheduledActions with a CLI The following code examples show how to use DescribeScheduledActions. CLI AWS CLI To describe scheduled actions The following describe-scheduled-actions example displays details for the scheduled actions for the speciﬁed service namespace: aws application-autoscaling describe-scheduled-actions \ --service-namespace dynamodb Output: { "ScheduledActions": [ { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Schedule": "at(2019-05-20T18:35:00)", "ResourceId": "table/my-table", "CreationTime": 1561571888.361, "ScheduledActionARN": "arn:aws:autoscaling:us- west-2:123456789012:scheduledAction:2d36aa3b-cdf9-4565- b290-81db519b227d:resource/dynamodb/table/my-table:scheduledActionName/my-first- scheduled-action", "ScalableTargetAction": { "MinCapacity": 15, "MaxCapacity": 20 }, "ScheduledActionName": "my-first-scheduled-action", Actions 184 Application Auto Scaling User Guide "ServiceNamespace": "dynamodb" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Schedule": "at(2019-05-20T18:40:00)", "ResourceId": "table/my-table", "CreationTime": 1561571946.021, "ScheduledActionARN": "arn:aws:autoscaling:us- west-2:123456789012:scheduledAction:2d36aa3b-cdf9-4565- b290-81db519b227d:resource/dynamodb/table/my-table:scheduledActionName/my-second- scheduled-action", "ScalableTargetAction": { "MinCapacity": 5, "MaxCapacity": 10 }, "ScheduledActionName": "my-second-scheduled-action", "ServiceNamespace": "dynamodb" } ] } For more information, see Scheduled Scaling in the Application Auto Scaling User Guide. • For API details, see DescribeScheduledActions in AWS CLI Command Reference. PowerShell Tools for PowerShell Example 1: This cmdlet lists the actions scheduled for your Auto Scaling group that haven't run or that have not reached their end time.
app-as-ug-053	app-as-ug.pdf	53	scheduled-action", "ScalableTargetAction": { "MinCapacity": 15, "MaxCapacity": 20 }, "ScheduledActionName": "my-first-scheduled-action", Actions 184 Application Auto Scaling User Guide "ServiceNamespace": "dynamodb" }, { "ScalableDimension": "dynamodb:table:WriteCapacityUnits", "Schedule": "at(2019-05-20T18:40:00)", "ResourceId": "table/my-table", "CreationTime": 1561571946.021, "ScheduledActionARN": "arn:aws:autoscaling:us- west-2:123456789012:scheduledAction:2d36aa3b-cdf9-4565- b290-81db519b227d:resource/dynamodb/table/my-table:scheduledActionName/my-second- scheduled-action", "ScalableTargetAction": { "MinCapacity": 5, "MaxCapacity": 10 }, "ScheduledActionName": "my-second-scheduled-action", "ServiceNamespace": "dynamodb" } ] } For more information, see Scheduled Scaling in the Application Auto Scaling User Guide. • For API details, see DescribeScheduledActions in AWS CLI Command Reference. PowerShell Tools for PowerShell Example 1: This cmdlet lists the actions scheduled for your Auto Scaling group that haven't run or that have not reached their end time. Get-AASScheduledAction -ServiceNamespace AppStream Output: CreationTime : 12/22/2019 9:25:52 AM EndTime : 1/1/0001 12:00:00 AM ResourceId : fleet/MyFleet ScalableDimension : appstream:fleet:DesiredCapacity ScalableTargetAction : Amazon.ApplicationAutoScaling.Model.ScalableTargetAction Schedule : cron(0 0 8 ? * MON-FRI *) Actions 185 Application Auto Scaling User Guide ScheduledActionARN : arn:aws:autoscaling:us- west-2:012345678912:scheduledAction:4897ca24-3caa-4bf1-8484-851a089b243c:resource/ appstream/fleet/MyFleet:scheduledActionName /WeekDaysFleetScaling ScheduledActionName : WeekDaysFleetScaling ServiceNamespace : appstream StartTime : 1/1/0001 12:00:00 AM • For API details, see DescribeScheduledActions in AWS Tools for PowerShell Cmdlet Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use PutScalingPolicy with a CLI The following code examples show how to use PutScalingPolicy. CLI AWS CLI Example 1: To apply a target tracking scaling policy with a predeﬁned metric speciﬁcation The following put-scaling-policy example applies a target tracking scaling policy with a predeﬁned metric speciﬁcation to an Amazon ECS service called web-app in the default cluster. The policy keeps the average CPU utilization of the service at 75 percent, with scale- out and scale-in cooldown periods of 60 seconds. The output contains the ARNs and names of the two CloudWatch alarms created on your behalf. aws application-autoscaling put-scaling-policy --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id service/default/web-app \ --policy-name cpu75-target-tracking-scaling-policy --policy- type TargetTrackingScaling \ --target-tracking-scaling-policy-configuration file://config.json This example assumes that you have a conﬁg.json ﬁle in the current directory with the following contents: Actions 186 Application Auto Scaling User Guide { "TargetValue": 75.0, "PredefinedMetricSpecification": { "PredefinedMetricType": "ECSServiceAverageCPUUtilization" }, "ScaleOutCooldown": 60, "ScaleInCooldown": 60 } Output: { "PolicyARN": "arn:aws:autoscaling:us- west-2:012345678910:scalingPolicy:6d8972f3-efc8-437c-92d1-6270f29a66e7:resource/ ecs/service/default/web-app:policyName/cpu75-target-tracking-scaling-policy", "Alarms": [ { "AlarmARN": "arn:aws:cloudwatch:us- west-2:012345678910:alarm:TargetTracking-service/default/web-app-AlarmHigh- d4f0770c-b46e-434a-a60f-3b36d653feca", "AlarmName": "TargetTracking-service/default/web-app-AlarmHigh- d4f0770c-b46e-434a-a60f-3b36d653feca" }, { "AlarmARN": "arn:aws:cloudwatch:us- west-2:012345678910:alarm:TargetTracking-service/default/web-app- AlarmLow-1b437334-d19b-4a63-a812-6c67aaf2910d", "AlarmName": "TargetTracking-service/default/web-app- AlarmLow-1b437334-d19b-4a63-a812-6c67aaf2910d" } ] } Example 2: To apply a target tracking scaling policy with a customized metric speciﬁcation The following put-scaling-policy example applies a target tracking scaling policy with a customized metric speciﬁcation to an Amazon ECS service called web-app in the default cluster. The policy keeps the average utilization of the service at 75 percent, with scale-out and scale-in cooldown periods of 60 seconds. The output contains the ARNs and names of the two CloudWatch alarms created on your behalf. Actions 187 Application Auto Scaling User Guide aws application-autoscaling put-scaling-policy --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id service/default/web-app \ --policy-name cms75-target-tracking-scaling-policy --policy-type TargetTrackingScaling \ --target-tracking-scaling-policy-configuration file://config.json This example assumes that you have a conﬁg.json ﬁle in the current directory with the following contents: { "TargetValue":75.0, "CustomizedMetricSpecification":{ "MetricName":"MyUtilizationMetric", "Namespace":"MyNamespace", "Dimensions": [ { "Name":"MyOptionalMetricDimensionName", "Value":"MyOptionalMetricDimensionValue" } ], "Statistic":"Average", "Unit":"Percent" }, "ScaleOutCooldown": 60, "ScaleInCooldown": 60 } Output: { "PolicyARN": "arn:aws:autoscaling:us-west-2:012345678910:scalingPolicy: 8784a896-b2ba-47a1-b08c-27301cc499a1:resource/ecs/service/default/web- app:policyName/cms75-target-tracking-scaling-policy", "Alarms": [ { "AlarmARN": "arn:aws:cloudwatch:us- west-2:012345678910:alarm:TargetTracking-service/default/web-app- AlarmHigh-9bc77b56-0571-4276-ba0f-d4178882e0a0", "AlarmName": "TargetTracking-service/default/web-app- AlarmHigh-9bc77b56-0571-4276-ba0f-d4178882e0a0" }, Actions 188 Application Auto Scaling { User Guide "AlarmARN": "arn:aws:cloudwatch:us- west-2:012345678910:alarm:TargetTracking-service/default/web-app- AlarmLow-9b6ad934-6d37-438e-9e05-02836ddcbdc4", "AlarmName": "TargetTracking-service/default/web-app- AlarmLow-9b6ad934-6d37-438e-9e05-02836ddcbdc4" } ] } Example 3: To apply a target tracking scaling policy for scale out only The following put-scaling-policy example applies a target tracking scaling policy to an Amazon ECS service called web-app in the default cluster. The policy is used to scale out the ECS service when the RequestCountPerTarget metric from the Application Load Balancer exceeds the threshold. The output contains the ARN and name of the CloudWatch alarm created on your behalf. aws application-autoscaling put-scaling-policy \ --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id service/default/web-app \ --policy-name alb-scale-out-target-tracking-scaling-policy \ --policy-type TargetTrackingScaling \ --target-tracking-scaling-policy-configuration file://config.json Contents of config.json: { "TargetValue": 1000.0, "PredefinedMetricSpecification": { "PredefinedMetricType": "ALBRequestCountPerTarget", "ResourceLabel": "app/EC2Co-EcsEl-1TKLTMITMM0EO/f37c06a68c1748aa/ targetgroup/EC2Co-Defau-LDNM7Q3ZH1ZN/6d4ea56ca2d6a18d" }, "ScaleOutCooldown": 60, "ScaleInCooldown": 60, "DisableScaleIn": true } Output: Actions 189 Application Auto Scaling User Guide { "PolicyARN": "arn:aws:autoscaling:us- west-2:123456789012:scalingPolicy:6d8972f3-efc8-437c-92d1-6270f29a66e7:resource/ ecs/service/default/web-app:policyName/alb-scale-out-target-tracking-scaling- policy", "Alarms": [ { "AlarmName": "TargetTracking-service/default/web-app-AlarmHigh- d4f0770c-b46e-434a-a60f-3b36d653feca", "AlarmARN": "arn:aws:cloudwatch:us- west-2:123456789012:alarm:TargetTracking-service/default/web-app-AlarmHigh- d4f0770c-b46e-434a-a60f-3b36d653feca" } ] } For more information, see Target Tracking Scaling Policies for Application Auto Scaling in the AWS Application Auto Scaling User Guide. • For API details, see PutScalingPolicy in AWS CLI Command Reference. PowerShell Tools for PowerShell Example 1: This cmdlet creates or updates a policy for an Application Auto Scaling scalable target. Each scalable target is identiﬁed by a service namespace, resource ID, and scalable dimension. Set-AASScalingPolicy -ServiceNamespace AppStream -PolicyName ASFleetScaleInPolicy -PolicyType StepScaling -ResourceId fleet/MyFleet -ScalableDimension appstream:fleet:DesiredCapacity -StepScalingPolicyConfiguration_AdjustmentType ChangeInCapacity -StepScalingPolicyConfiguration_Cooldown 360 -StepScalingPolicyConfiguration_MetricAggregationType Average - StepScalingPolicyConfiguration_StepAdjustments @{ScalingAdjustment = -1; MetricIntervalUpperBound = 0} Output: Alarms PolicyARN ------ --------- Actions 190 Application Auto Scaling User
app-as-ug-054	app-as-ug.pdf	54	more information, see Target Tracking Scaling Policies for Application Auto Scaling in the AWS Application Auto Scaling User Guide. • For API details, see PutScalingPolicy in AWS CLI Command Reference. PowerShell Tools for PowerShell Example 1: This cmdlet creates or updates a policy for an Application Auto Scaling scalable target. Each scalable target is identiﬁed by a service namespace, resource ID, and scalable dimension. Set-AASScalingPolicy -ServiceNamespace AppStream -PolicyName ASFleetScaleInPolicy -PolicyType StepScaling -ResourceId fleet/MyFleet -ScalableDimension appstream:fleet:DesiredCapacity -StepScalingPolicyConfiguration_AdjustmentType ChangeInCapacity -StepScalingPolicyConfiguration_Cooldown 360 -StepScalingPolicyConfiguration_MetricAggregationType Average - StepScalingPolicyConfiguration_StepAdjustments @{ScalingAdjustment = -1; MetricIntervalUpperBound = 0} Output: Alarms PolicyARN ------ --------- Actions 190 Application Auto Scaling User Guide {} arn:aws:autoscaling:us- west-2:012345678912:scalingPolicy:4897ca24-3caa-4bf1-8484-851a089b243c:resource/ appstream/fleet/MyFleet:policyName/ASFleetScaleInPolicy • For API details, see PutScalingPolicy in AWS Tools for PowerShell Cmdlet Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use PutScheduledAction with a CLI The following code examples show how to use PutScheduledAction. CLI AWS CLI To add a scheduled action to a DynamoDB table This example adds a scheduled action to a DynamoDB table called TestTable to scale out on a recurring schedule. On the speciﬁed schedule (every day at 12:15pm UTC), if the current capacity is below the value speciﬁed for MinCapacity, Application Auto Scaling scales out to the value speciﬁed by MinCapacity. Command: aws application-autoscaling put-scheduled-action --service- namespace dynamodb --scheduled-action-name my-recurring-action -- schedule "cron(15 12 * * ? )" --resource-id table/TestTable -- scalable-dimension dynamodb:table:WriteCapacityUnits --scalable-target- action MinCapacity=6 For more information, see Scheduled Scaling in the Application Auto Scaling User Guide. • For API details, see PutScheduledAction in AWS CLI Command Reference. Actions 191 Application Auto Scaling PowerShell Tools for PowerShell User Guide Example 1: This cmdlet creates or updates a scheduled action for an Application Auto Scaling scalable target. Each scalable target is identiﬁed by a service namespace, resource ID, and scalable dimension. Set-AASScheduledAction -ServiceNamespace AppStream -ResourceId fleet/ MyFleet -Schedule "cron(0 0 8 ? MON-FRI )" -ScalableDimension appstream:fleet:DesiredCapacity -ScheduledActionName WeekDaysFleetScaling - ScalableTargetAction_MinCapacity 5 -ScalableTargetAction_MaxCapacity 10 • For API details, see PutScheduledAction in AWS Tools for PowerShell Cmdlet Reference. For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Use RegisterScalableTarget with an AWS SDK or CLI The following code examples show how to use RegisterScalableTarget. CLI AWS CLI Example 1: To register an ECS service as a scalable target The following register-scalable-target example registers an Amazon ECS service with Application Auto Scaling. It also adds a tag with the key name environment and the value production to the scalable target. aws application-autoscaling register-scalable-target \ --service-namespace ecs \ --scalable-dimension ecs:service:DesiredCount \ --resource-id service/default/web-app \ --min-capacity 1 --max-capacity 10 \ --tags environment=production Output: Actions 192 Application Auto Scaling User Guide { "ScalableTargetARN": "arn:aws:application-autoscaling:us- west-2:123456789012:scalable-target/1234abcd56ab78cd901ef1234567890ab123" } For examples for other AWS services and custom resources, see the topics in AWS services that you can use with Application Auto Scaling in the Application Auto Scaling User Guide. Example 2: To suspend scaling activities for a scalable target The following register-scalable-target example suspends scaling activities for an existing scalable target. aws application-autoscaling register-scalable-target \ --service-namespace dynamodb \ --scalable-dimension dynamodb:table:ReadCapacityUnits \ --resource-id table/my-table \ --suspended- state DynamicScalingInSuspended=true,DynamicScalingOutSuspended=true,ScheduledScalingSuspended=true Output: { "ScalableTargetARN": "arn:aws:application-autoscaling:us- west-2:123456789012:scalable-target/1234abcd56ab78cd901ef1234567890ab123" } For more information, see Suspending and resuming scaling for Application Auto Scaling in the Application Auto Scaling User Guide. Example 3: To resume scaling activities for a scalable target The following register-scalable-target example resumes scaling activities for an existing scalable target. aws application-autoscaling register-scalable-target \ --service-namespace dynamodb \ --scalable-dimension dynamodb:table:ReadCapacityUnits \ --resource-id table/my-table \ --suspended- state DynamicScalingInSuspended=false,DynamicScalingOutSuspended=false,ScheduledScalingSuspended=false Actions 193 Application Auto Scaling Output: { User Guide "ScalableTargetARN": "arn:aws:application-autoscaling:us- west-2:123456789012:scalable-target/1234abcd56ab78cd901ef1234567890ab123" } For more information, see Suspending and resuming scaling for Application Auto Scaling in the Application Auto Scaling User Guide. • For API details, see RegisterScalableTarget in AWS CLI Command Reference. Java SDK for Java 2.x Note There's more on GitHub. Find the complete example and learn how to set up and run in the AWS Code Examples Repository. import software.amazon.awssdk.regions.Region; import software.amazon.awssdk.services.applicationautoscaling.ApplicationAutoScalingClient; import software.amazon.awssdk.services.applicationautoscaling.model.ApplicationAutoScalingException; import software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalableTargetsRequest; import software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalableTargetsResponse; import software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalingPoliciesRequest; import software.amazon.awssdk.services.applicationautoscaling.model.DescribeScalingPoliciesResponse; import software.amazon.awssdk.services.applicationautoscaling.model.PolicyType; import software.amazon.awssdk.services.applicationautoscaling.model.PredefinedMetricSpecification; import software.amazon.awssdk.services.applicationautoscaling.model.PutScalingPolicyRequest; import software.amazon.awssdk.services.applicationautoscaling.model.RegisterScalableTargetRequest; Actions 194 Application Auto Scaling import User Guide software.amazon.awssdk.services.applicationautoscaling.model.ScalingPolicy; import software.amazon.awssdk.services.applicationautoscaling.model.ServiceNamespace; import software.amazon.awssdk.services.applicationautoscaling.model.ScalableDimension; import software.amazon.awssdk.services.applicationautoscaling.model.MetricType; import software.amazon.awssdk.services.applicationautoscaling.model.TargetTrackingScalingPolicyConfiguration; import java.util.List; /* * Before running this Java V2 code example, set up your development environment, including your credentials. * * For more information, see the following documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get- started.html */ public class EnableDynamoDBAutoscaling { public static void main(String[] args) { final String usage = """ Usage: <tableId> <roleARN> <policyName>\s Where: tableId - The table Id value (for example, table/Music). roleARN - The ARN of the role that has ApplicationAutoScaling permissions. policyName
app-as-ug-055	app-as-ug.pdf	55	import software.amazon.awssdk.services.applicationautoscaling.model.PolicyType; import software.amazon.awssdk.services.applicationautoscaling.model.PredefinedMetricSpecification; import software.amazon.awssdk.services.applicationautoscaling.model.PutScalingPolicyRequest; import software.amazon.awssdk.services.applicationautoscaling.model.RegisterScalableTargetRequest; Actions 194 Application Auto Scaling import User Guide software.amazon.awssdk.services.applicationautoscaling.model.ScalingPolicy; import software.amazon.awssdk.services.applicationautoscaling.model.ServiceNamespace; import software.amazon.awssdk.services.applicationautoscaling.model.ScalableDimension; import software.amazon.awssdk.services.applicationautoscaling.model.MetricType; import software.amazon.awssdk.services.applicationautoscaling.model.TargetTrackingScalingPolicyConfiguration; import java.util.List; /** * Before running this Java V2 code example, set up your development environment, including your credentials. * * For more information, see the following documentation topic: * * https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/get- started.html */ public class EnableDynamoDBAutoscaling { public static void main(String[] args) { final String usage = """ Usage: <tableId> <roleARN> <policyName>\s Where: tableId - The table Id value (for example, table/Music). roleARN - The ARN of the role that has ApplicationAutoScaling permissions. policyName - The name of the policy to create. """; if (args.length != 3) { System.out.println(usage); System.exit(1); } System.out.println("This example registers an Amazon DynamoDB table, which is the resource to scale."); String tableId = args[0]; String roleARN = args[1]; Actions 195 Application Auto Scaling User Guide String policyName = args[2]; ServiceNamespace ns = ServiceNamespace.DYNAMODB; ScalableDimension tableWCUs = ScalableDimension.DYNAMODB_TABLE_WRITE_CAPACITY_UNITS; ApplicationAutoScalingClient appAutoScalingClient = ApplicationAutoScalingClient.builder() .region(Region.US_EAST_1) .build(); registerScalableTarget(appAutoScalingClient, tableId, roleARN, ns, tableWCUs); verifyTarget(appAutoScalingClient, tableId, ns, tableWCUs); configureScalingPolicy(appAutoScalingClient, tableId, ns, tableWCUs, policyName); } public static void registerScalableTarget(ApplicationAutoScalingClient appAutoScalingClient, String tableId, String roleARN, ServiceNamespace ns, ScalableDimension tableWCUs) { try { RegisterScalableTargetRequest targetRequest = RegisterScalableTargetRequest.builder() .serviceNamespace(ns) .scalableDimension(tableWCUs) .resourceId(tableId) .roleARN(roleARN) .minCapacity(5) .maxCapacity(10) .build(); appAutoScalingClient.registerScalableTarget(targetRequest); System.out.println("You have registered " + tableId); } catch (ApplicationAutoScalingException e) { System.err.println(e.awsErrorDetails().errorMessage()); } } // Verify that the target was created. public static void verifyTarget(ApplicationAutoScalingClient appAutoScalingClient, String tableId, ServiceNamespace ns, ScalableDimension tableWCUs) { DescribeScalableTargetsRequest dscRequest = DescribeScalableTargetsRequest.builder() Actions 196 Application Auto Scaling User Guide .scalableDimension(tableWCUs) .serviceNamespace(ns) .resourceIds(tableId) .build(); DescribeScalableTargetsResponse response = appAutoScalingClient.describeScalableTargets(dscRequest); System.out.println("DescribeScalableTargets result: "); System.out.println(response); } // Configure a scaling policy. public static void configureScalingPolicy(ApplicationAutoScalingClient appAutoScalingClient, String tableId, ServiceNamespace ns, ScalableDimension tableWCUs, String policyName) { // Check if the policy exists before creating a new one. DescribeScalingPoliciesResponse describeScalingPoliciesResponse = appAutoScalingClient.describeScalingPolicies(DescribeScalingPoliciesRequest.builder() .serviceNamespace(ns) .resourceId(tableId) .scalableDimension(tableWCUs) .build()); if (!describeScalingPoliciesResponse.scalingPolicies().isEmpty()) { // If policies exist, consider updating an existing policy instead of creating a new one. System.out.println("Policy already exists. Consider updating it instead."); List<ScalingPolicy> polList = describeScalingPoliciesResponse.scalingPolicies(); for (ScalingPolicy pol : polList) { System.out.println("Policy name:" +pol.policyName()); } } else { // If no policies exist, proceed with creating a new policy. PredefinedMetricSpecification specification = PredefinedMetricSpecification.builder() .predefinedMetricType(MetricType.DYNAMO_DB_WRITE_CAPACITY_UTILIZATION) .build(); TargetTrackingScalingPolicyConfiguration policyConfiguration = TargetTrackingScalingPolicyConfiguration.builder() .predefinedMetricSpecification(specification) Actions 197 Application Auto Scaling User Guide .targetValue(50.0) .scaleInCooldown(60) .scaleOutCooldown(60) .build(); PutScalingPolicyRequest putScalingPolicyRequest = PutScalingPolicyRequest.builder() .targetTrackingScalingPolicyConfiguration(policyConfiguration) .serviceNamespace(ns) .scalableDimension(tableWCUs) .resourceId(tableId) .policyName(policyName) .policyType(PolicyType.TARGET_TRACKING_SCALING) .build(); try { appAutoScalingClient.putScalingPolicy(putScalingPolicyRequest); System.out.println("You have successfully created a scaling policy for an Application Auto Scaling scalable target"); } catch (ApplicationAutoScalingException e) { System.err.println("Error: " + e.awsErrorDetails().errorMessage()); } } } } • For API details, see RegisterScalableTarget in AWS SDK for Java 2.x API Reference. PowerShell Tools for PowerShell Example 1: This cmdlet registers or updates a scalable target. A scalable target is a resource that Application Auto Scaling can scale out and scale in. Add-AASScalableTarget -ServiceNamespace AppStream -ResourceId fleet/MyFleet - ScalableDimension appstream:fleet:DesiredCapacity -MinCapacity 2 -MaxCapacity 10 • For API details, see RegisterScalableTarget in AWS Tools for PowerShell Cmdlet Reference. Actions 198 Application Auto Scaling User Guide For a complete list of AWS SDK developer guides and code examples, see Using this service with an AWS SDK. This topic also includes information about getting started and details about previous SDK versions. Actions 199 Application Auto Scaling User Guide Tagging support for Application Auto Scaling You can use the AWS CLI or an SDK to tag Application Auto Scaling scalable targets. Scalable targets are the entities that represent the AWS or custom resources that Application Auto Scaling can scale. Each tag is a label consisting of a user-deﬁned key and value using the Application Auto Scaling API. Tags can help you conﬁgure granular access to speciﬁc scalable targets according to your organization's needs. For more information, see ABAC with Application Auto Scaling. You can add tags to new scalable targets when you register them, or you can add them to existing scalable targets. The commonly used commands for managing tags include: • register-scalable-target to tag new scalable targets when you register them. • tag-resource to add tags to an existing scalable target. • list-tags-for-resource to return the tags on a scalable target. • untag-resource to delete a tag. Tagging example Use the following register-scalable-target command with the --tags option. This example tags a scalable target with two tags: a tag key named environment with the tag value of production, and a tag key named iscontainerbased with the tag value of true. Replace the sample values for --min-capacity and --max-capacity and sample text for -- service-namespace with the namespace of the AWS service you're using with Application Auto Scaling, --scalable-dimension with the scalable dimension associated with the resource you're registering, and --resource-id with an identiﬁer for the resource. For more information and examples for each service, see the topics in AWS services that you can use with Application Auto Scaling. aws application-autoscaling register-scalable-target \ --service-namespace namespace
app-as-ug-056	app-as-ug.pdf	56	with two tags: a tag key named environment with the tag value of production, and a tag key named iscontainerbased with the tag value of true. Replace the sample values for --min-capacity and --max-capacity and sample text for -- service-namespace with the namespace of the AWS service you're using with Application Auto Scaling, --scalable-dimension with the scalable dimension associated with the resource you're registering, and --resource-id with an identiﬁer for the resource. For more information and examples for each service, see the topics in AWS services that you can use with Application Auto Scaling. aws application-autoscaling register-scalable-target \ --service-namespace namespace \ --scalable-dimension dimension \ --resource-id identifier \ Tagging example 200 Application Auto Scaling User Guide --min-capacity 1 --max-capacity 10 \ --tags environment=production,iscontainerbased=true If successful, this command returns the ARN of the scalable target. { "ScalableTargetARN": "arn:aws:application-autoscaling:region:account-id:scalable- target/1234abcd56ab78cd901ef1234567890ab123" } Note If this command throws an error, make sure that you have updated the AWS CLI locally to the latest version. Tags for security Use tags to verify that the requester (such as an IAM user or role) has permissions to perform certain actions. Provide tag information in the condition element of an IAM policy by using one or more of the following condition keys: • Use aws:ResourceTag/tag-key: tag-value to allow (or deny) user actions on scalable targets with speciﬁc tags. • Use aws:RequestTag/tag-key: tag-value to require that a speciﬁc tag be present (or not present) in a request. • Use aws:TagKeys [tag-key, ...] to require that speciﬁc tag keys be present (or not present) in a request. For example, the following IAM policy grants permissions to use the DeregisterScalableTarget, DeleteScalingPolicy, and DeleteScheduledAction actions. However, it also denies the actions if the scalable target being acted upon has the tag environment=production. { "Version": "2012-10-17", "Statement": [ { Tags for security 201 Application Auto Scaling User Guide "Effect": "Allow", "Action": [ "application-autoscaling:DeregisterScalableTarget", "application-autoscaling:DeleteScalingPolicy", "application-autoscaling:DeleteScheduledAction" ], "Resource": "" } }, { "Effect": "Deny", "Action": [ "application-autoscaling:DeregisterScalableTarget", "application-autoscaling:DeleteScalingPolicy", "application-autoscaling:DeleteScheduledAction" ], "Resource": "", "Condition": { "StringEquals": {"aws:ResourceTag/environment": "production"} } } ] } Control access to tags Use tags to verify that the requester (such as an IAM user or role) has permissions to add, modify, or delete tags for scalable targets. For example, you could create an IAM policy that allows removing only the tag with the temporary key from scalable targets. { "Version": "2012-10-17", "Statement": [ { "Effect": "Allow", "Action": "application-autoscaling:UntagResource", "Resource": "*", "Condition": { "ForAllValues:StringEquals": { "aws:TagKeys": ["temporary"] } } Control access to tags 202 Application Auto Scaling } ] } User Guide Control access to tags 203 Application Auto Scaling User Guide Security in Application Auto Scaling Cloud security at AWS is the highest priority. As an AWS customer, you beneﬁt from a data center and network architecture that is built to meet the requirements of the most security-sensitive organizations. Security is a shared responsibility between AWS and you. The shared responsibility model describes this as security of the cloud and security in the cloud: • Security of the cloud – AWS is responsible for protecting the infrastructure that runs AWS services in the AWS Cloud. AWS also provides you with services that you can use securely. Third- party auditors regularly test and verify the eﬀectiveness of our security as part of the AWS compliance programs. To learn about the compliance programs that apply to Application Auto Scaling, see AWS services in scope by compliance program. • Security in the cloud – Your responsibility is determined by the AWS service that you use. You are also responsible for other factors including the sensitivity of your data, your company's requirements, and applicable laws and regulations. This documentation helps you understand how to apply the shared responsibility model when using Application Auto Scaling. The following topics show you how to conﬁgure Application Auto Scaling to meet your security and compliance objectives. You also learn how to use other AWS services that help you to monitor and secure your Application Auto Scaling resources. Contents • Data protection in Application Auto Scaling • Identity and Access Management for Application Auto Scaling • Access Application Auto Scaling using interface VPC endpoints • Resilience in Application Auto Scaling • Infrastructure security in Application Auto Scaling • Compliance validation for Application Auto Scaling Data protection in Application Auto Scaling The AWS shared responsibility model applies to data protection in Application Auto Scaling. As described in this model, AWS is responsible for protecting the global infrastructure that runs all Data protection 204 Application Auto Scaling User Guide of the AWS Cloud. You are responsible for maintaining control over your content that is hosted on this infrastructure. You are also responsible for the security conﬁguration and management tasks for the AWS services that you use. For more information about data privacy, see the Data Privacy FAQ. For information about data protection in
app-as-ug-057	app-as-ug.pdf	57	Application Auto Scaling Data protection in Application Auto Scaling The AWS shared responsibility model applies to data protection in Application Auto Scaling. As described in this model, AWS is responsible for protecting the global infrastructure that runs all Data protection 204 Application Auto Scaling User Guide of the AWS Cloud. You are responsible for maintaining control over your content that is hosted on this infrastructure. You are also responsible for the security conﬁguration and management tasks for the AWS services that you use. For more information about data privacy, see the Data Privacy FAQ. For information about data protection in Europe, see the AWS Shared Responsibility Model and GDPR blog post on the AWS Security Blog. For data protection purposes, we recommend that you protect AWS account credentials and set up individual users with AWS IAM Identity Center or AWS Identity and Access Management (IAM). That way, each user is given only the permissions necessary to fulﬁll their job duties. We also recommend that you secure your data in the following ways: • Use multi-factor authentication (MFA) with each account. • Use SSL/TLS to communicate with AWS resources. We require TLS 1.2 and recommend TLS 1.3. • Set up API and user activity logging with AWS CloudTrail. For information about using CloudTrail trails to capture AWS activities, see Working with CloudTrail trails in the AWS CloudTrail User Guide. • Use AWS encryption solutions, along with all default security controls within AWS services. • Use advanced managed security services such as Amazon Macie, which assists in discovering and securing sensitive data that is stored in Amazon S3. • If you require FIPS 140-3 validated cryptographic modules when accessing AWS through a command line interface or an API, use a FIPS endpoint. For more information about the available FIPS endpoints, see Federal Information Processing Standard (FIPS) 140-3. We strongly recommend that you never put conﬁdential or sensitive information, such as your customers' email addresses, into tags or free-form text ﬁelds such as a Name ﬁeld. This includes when you work with Application Auto Scaling or other AWS services using the console, API, AWS CLI, or AWS SDKs. Any data that you enter into tags or free-form text ﬁelds used for names may be used for billing or diagnostic logs. If you provide a URL to an external server, we strongly recommend that you do not include credentials information in the URL to validate your request to that server. Identity and Access Management for Application Auto Scaling AWS Identity and Access Management (IAM) is an AWS service that helps an administrator securely control access to AWS resources. IAM administrators control who can be authenticated (signed in) Identity and Access Management 205 Application Auto Scaling User Guide and authorized (have permissions) to use Application Auto Scaling resources. IAM is an AWS service that you can use with no additional charge. For complete IAM documentation, see the IAM User Guide. Access control You can have valid credentials to authenticate your requests, but unless you have permissions you cannot create or access Application Auto Scaling resources. For example, you must have permissions to create scaling policies, conﬁgure scheduled scaling, and so on. The following sections provide details on how an IAM administrator can use IAM to help secure your AWS resources, by controlling who can perform Application Auto Scaling API actions. Contents • How Application Auto Scaling works with IAM • AWS managed policies for Application Auto Scaling • Service-linked roles for Application Auto Scaling • Application Auto Scaling identity-based policy examples • Troubleshooting access to Application Auto Scaling • Permissions validation for Application Auto Scaling API calls on target resources How Application Auto Scaling works with IAM Note In December 2017, there was an update for Application Auto Scaling, enabling several service-linked roles for Application Auto Scaling integrated services. Speciﬁc IAM permissions and an Application Auto Scaling service-linked role (or a service role for Amazon EMR auto scaling) are required so that users can conﬁgure scaling. Before you use IAM to manage access to Application Auto Scaling, learn what IAM features are available to use with Application Auto Scaling. Access control 206 Application Auto Scaling User Guide IAM features you can use with Application Auto Scaling IAM feature Application Auto Scaling support Identity-based policies Policy actions Policy resources Policy condition keys (service-speciﬁc) Resource-based policies ACLs Yes Yes Yes Yes No No ABAC (tags in policies) Partial Temporary credentials Service roles Service-linked roles Yes Yes Yes To get a high-level view of how Application Auto Scaling and other AWS services work with most IAM features, see AWS services that work with IAM in the IAM User Guide. Application Auto Scaling identity-based policies Supports identity-based policies: Yes Identity-based policies are JSON permissions policy documents that you can attach to an identity, such as an IAM user, group

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.