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
Amazon SageMaker Serverless endpoint provisioned concurrency
Amazon SageMaker inference components
Spot Fleets (Amazon EC2)
Custom resources provided by your own applications or services
To learn more about Application Auto Scaling, see the Application Auto Scaling User Guide.
API Summary
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
register_scalable_target
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
scaling.
applicationautoscaling(
config = list(),
credentials = list(),
endpoint = NULL,
region = NULL
)A client for the service. You can call the service's operations using
syntax like svc$operation(...), where svc is the name you've assigned
to the client. The available operations are listed in the
Operations section.
Optional configuration of credentials, endpoint, and/or region.
credentials:
creds:
access_key_id: AWS access key ID
secret_access_key: AWS secret access key
session_token: AWS temporary session token
profile: The name of a profile to use. If not given, then the default profile is used.
anonymous: Set anonymous credentials.
endpoint: The complete URL to use for the constructed client.
region: The AWS Region used in instantiating the client.
close_connection: Immediately close all HTTP connections.
timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.
s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. http://s3.amazonaws.com/BUCKET/KEY.
sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html
Optional credentials shorthand for the config parameter
creds:
access_key_id: AWS access key ID
secret_access_key: AWS secret access key
session_token: AWS temporary session token
profile: The name of a profile to use. If not given, then the default profile is used.
anonymous: Set anonymous credentials.
Optional shorthand for complete URL to use for the constructed client.
Optional shorthand for AWS Region used in instantiating the client.
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",
sts_regional_endpoint = "string"
),
credentials = list(
creds = list(
access_key_id = "string",
secret_access_key = "string",
session_token = "string"
),
profile = "string",
anonymous = "logical"
),
endpoint = "string",
region = "string"
)
| 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 |
| list_tags_for_resource | Returns all the tags on the specified Application Auto Scaling scalable target |
| 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, which is the resource that you want to scale |
| tag_resource | Adds or edits tags on an Application Auto Scaling scalable target |
| untag_resource | Deletes tags from an Application Auto Scaling scalable target |
if (FALSE) {
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"
)
}
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