putScalingPolicy

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 scale out again.

For more information, see Target tracking scaling policies and Step scaling policies in the Application Auto Scaling User Guide.

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.

Samples

fun main() { 
   //sampleStart 
   // The following 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.
val resp = applicationAutoScalingClient.putScalingPolicy {
    policyName = "cpu75-target-tracking-scaling-policy"
    serviceNamespace = ServiceNamespace.fromValue("ecs")
    resourceId = "service/default/web-app"
    scalableDimension = ScalableDimension.fromValue("ecs:service:DesiredCount")
    policyType = PolicyType.fromValue("TargetTrackingScaling")
    targetTrackingScalingPolicyConfiguration = TargetTrackingScalingPolicyConfiguration {
        targetValue = 75.toDouble()
        predefinedMetricSpecification = PredefinedMetricSpecification {
            predefinedMetricType = MetricType.fromValue("ECSServiceAverageCPUUtilization")
        }
        scaleOutCooldown = 60
        scaleInCooldown = 60
    }
} 
   //sampleEnd
}