View source: R/applicationautoscaling_service.R
With Application Auto Scaling, you can configure automatic scaling for the following resources:
Amazon AppStream 2.0 fleets
Amazon Aurora Replicas
Amazon Comprehend document classification and entity recognizer endpoints
Amazon DynamoDB tables and global secondary indexes throughput capacity
Amazon ECS services
Amazon ElastiCache for Redis clusters (replication groups)
Amazon EMR clusters
Amazon Keyspaces (for Apache Cassandra) tables
Lambda function provisioned concurrency
Amazon Managed Streaming for Apache Kafka broker storage
Amazon Neptune clusters
Amazon SageMaker endpoint variants
Spot Fleets (Amazon EC2)
Custom resources provided by your own applications or services
The Application Auto Scaling service API includes three key sets of actions:
Register and manage scalable targets - Register Amazon Web Services or custom resources as scalable targets (a resource that Application Auto Scaling can scale), set minimum and maximum capacity limits, and retrieve information on existing scalable targets.
Configure and manage automatic scaling - Define scaling policies to dynamically scale your resources in response to CloudWatch alarms, schedule one-time or recurring scaling actions, and retrieve your recent scaling activity history.
Suspend and resume scaling - Temporarily suspend and later resume
automatic scaling by calling the
API action for any Application Auto Scaling scalable target. You can
suspend and resume (individually or in combination) scale-out
activities that are triggered by a scaling policy, scale-in
activities that are triggered by a scaling policy, and scheduled
To learn more about Application Auto Scaling, including information about granting IAM users required permissions for Application Auto Scaling actions, see the Application Auto Scaling User Guide.
applicationautoscaling(config = list())
Optional configuration of credentials, endpoint, and/or region.
A client for the service. You can call the service's operations using
svc is the name you've assigned
to the client. The available operations are listed in the
svc <- applicationautoscaling( config = list( credentials = list( creds = list( access_key_id = "string", secret_access_key = "string", session_token = "string" ), profile = "string", anonymous = "logical" ), endpoint = "string", region = "string", close_connection = "logical", timeout = "numeric", s3_force_path_style = "logical" ) )
|delete_scaling_policy||Deletes the specified scaling policy for an Application Auto Scaling scalable target|
|delete_scheduled_action||Deletes the specified scheduled action for an Application Auto Scaling scalable target|
|deregister_scalable_target||Deregisters an Application Auto Scaling scalable target when you have finished using it|
|describe_scalable_targets||Gets information about the scalable targets in the specified namespace|
|describe_scaling_activities||Provides descriptive information about the scaling activities in the specified namespace from the previous six weeks|
|describe_scaling_policies||Describes the Application Auto Scaling scaling policies for the specified service namespace|
|describe_scheduled_actions||Describes the Application Auto Scaling scheduled actions for the specified service namespace|
|put_scaling_policy||Creates or updates a scaling policy for an Application Auto Scaling scalable target|
|put_scheduled_action||Creates or updates a scheduled action for an Application Auto Scaling scalable target|
|register_scalable_target||Registers or updates a scalable target|
## Not run: svc <- applicationautoscaling() # This example deletes a scaling policy for the Amazon ECS service called # web-app, which is running in the default cluster. svc$delete_scaling_policy( PolicyName = "web-app-cpu-lt-25", ResourceId = "service/default/web-app", ScalableDimension = "ecs:service:DesiredCount", ServiceNamespace = "ecs" ) ## End(Not run)
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