PutScalingPolicy

Creates or updates a scaling policy for a fleet. Scaling policies are used to automatically scale a fleet's hosting capacity to meet player demand. An active scaling policy instructs Amazon GameLift to track a fleet metric and automatically change the fleet's capacity when a certain threshold is reached. There are two types of scaling policies: target-based and rule-based. Use a target-based policy to quickly and efficiently manage fleet scaling; this option is the most commonly used. Use rule-based policies when you need to exert fine-grained control over auto-scaling.

Fleets can have multiple scaling policies of each type in force at the same time; you can have one target-based policy, one or multiple rule-based scaling policies, or both. We recommend caution, however, because multiple auto-scaling policies can have unintended consequences.

Learn more about how to work with auto-scaling in Set Up Fleet Automatic Scaling.

Target-based policy

A target-based policy tracks a single metric: PercentAvailableGameSessions. This metric tells us how much of a fleet's hosting capacity is ready to host game sessions but is not currently in use. This is the fleet's buffer; it measures the additional player demand that the fleet could handle at current capacity. With a target-based policy, you set your ideal buffer size and leave it to Amazon GameLift to take whatever action is needed to maintain that target.

For example, you might choose to maintain a 10% buffer for a fleet that has the capacity to host 100 simultaneous game sessions. This policy tells Amazon GameLift to take action whenever the fleet's available capacity falls below or rises above 10 game sessions. Amazon GameLift will start new instances or stop unused instances in order to return to the 10% buffer.

To create or update a target-based policy, specify a fleet ID and name, and set the policy type to "TargetBased". Specify the metric to track (PercentAvailableGameSessions) and reference a TargetConfiguration object with your desired buffer value. Exclude all other parameters. On a successful request, the policy name is returned. The scaling policy is automatically in force as soon as it's successfully created. If the fleet's auto-scaling actions are temporarily suspended, the new policy will be in force once the fleet actions are restarted.

Rule-based policy

A rule-based policy tracks specified fleet metric, sets a threshold value, and specifies the type of action to initiate when triggered. With a rule-based policy, you can select from several available fleet metrics. Each policy specifies whether to scale up or scale down (and by how much), so you need one policy for each type of action.

For example, a policy may make the following statement: "If the percentage of idle instances is greater than 20% for more than 15 minutes, then reduce the fleet capacity by 10%."

A policy's rule statement has the following structure:

If [MetricName] is [ComparisonOperator] [Threshold] for [EvaluationPeriods] minutes, then [ScalingAdjustmentType] to/by [ScalingAdjustment].

To implement the example, the rule statement would look like this:

If [PercentIdleInstances] is [GreaterThanThreshold] [20] for [15] minutes, then [PercentChangeInCapacity] to/by [10].

To create or update a scaling policy, specify a unique combination of name and fleet ID, and set the policy type to "RuleBased". Specify the parameter values for a policy rule statement. On a successful request, the policy name is returned. Scaling policies are automatically in force as soon as they're successfully created. If the fleet's auto-scaling actions are temporarily suspended, the new policy will be in force once the fleet actions are restarted.

Request Syntax

{
   "ComparisonOperator": "string",
   "EvaluationPeriods": number,
   "FleetId": "string",
   "MetricName": "string",
   "Name": "string",
   "PolicyType": "string",
   "ScalingAdjustment": number,
   "ScalingAdjustmentType": "string",
   "TargetConfiguration": { 
      "TargetValue": number
   },
   "Threshold": 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.

Response Syntax

{
   "Name": "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.

Errors

For information about the errors that are common to all actions, see Common Errors.

Examples

Create a target-based scaling policy

This example sets up auto-scaling using a target-based scaling policy. For this fleet, we want to maintain a 15% capacity buffer for our game, so that our fleet will always be able to immediately accommodate some additional game sessions. For a target-based policy, we need to specify a fleet ID, policy name and type, metric name (set this parameter to "PercentAvailableGameSessions"), and target configuration (buffer size).

HTTP requests are authenticated using an AWS Signature Version 4 signature in the Authorization header field.

Sample Request

POST / HTTP/1.1
Host: gamelift.us-west-2.amazonaws.com;
Accept-Encoding: identity
Content-Length: 338
User-Agent: aws-cli/1.11.36 Python/2.7.9 Windows/7 botocore/1.4.93
Content-Type: application/x-amz-json-1.0
Authorization: AWS4-HMAC-SHA256  Credential=AKIAIOSFODNN7EXAMPLE/20170406/us-west-2/gamelift/aws4_request, SignedHeaders=content-type;host;x-amz-date;x-amz-target, Signature=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
X-Amz-Date: 20170406T004805Z
X-Amz-Target: GameLift.PutScalingPolicy 

{
    "FleetId": "fleet-2222bbbb-33cc-44dd-55ee-6666ffff77aa",
    "Name": "My_Target_Policy_1",
    "PolicyType": "TargetBased",
    "MetricName": "PercentAvailableGameSessions",
    "TargetConfiguration": {"TargetValue": 15}
}

CLI syntax:

$aws gamelift put-scaling-policy
--fleet-id "fleet-2222bbbb-33cc-44dd-55ee-6666ffff77aa"
--name "My_Target_Policy_1"
--policy-type "TargetBased" 
--metric-name "PercentAvailableGameSessions"
--target-configuration "TargetValue=5"

Sample Response

HTTP/1.1 200 OK
x-amzn-RequestId: b34f8665-EXAMPLE
Content-Type: application/x-amz-json-1.1
Content-Length: 607
Date: Thu, 06 Apr 2017 00:48:07 GMT

{
    "Name": "My_Target_Policy_1" 
}

Create a rule-based scaling policy

This example illustrates using a rule-based policy to supplement a target-based policy. While the target policy does most of the work of ensuring that capacity tracks with player demand, a well-formed rule-based policy can handle special circumstances and edge cases. For example, the target-based approach becomes less efficient when fleets have just few instances. We can mitigate this issue by creating a rule that maintains at least one idle instance ready to host new game sessions. At low capacity, the two policies do not conflict; at higher capacity, the rule-based policy loses relevance.

HTTP requests are authenticated using an AWS Signature Version 4 signature in the Authorization header field.

Sample Request

POST / HTTP/1.1
Host: gamelift.us-west-2.amazonaws.com;
Accept-Encoding: identity
Content-Length: 336
User-Agent: aws-cli/1.11.36 Python/2.7.9 Windows/7 botocore/1.4.93
Content-Type: application/x-amz-json-1.0
Authorization: AWS4-HMAC-SHA256  Credential=AKIAIOSFODNN7EXAMPLE/20170406/us-west-2/gamelift/aws4_request, SignedHeaders=content-type;host;x-amz-date;x-amz-target, Signature=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
X-Amz-Date: 20170406T004805Z
X-Amz-Target: GameLift.PutScalingPolicy 

{
    "FleetId": "fleet-2222bbbb-33cc-44dd-55ee-6666ffff77aa",
    "Name": "My_Rule_Policy_1",
    "PolicyType": "RuleBased",
    "MetricName": "IdleInstances",
    "ComparisonOperator": "LessThanThreshold",
    "Threshold": "2" 
    "EvaluationPeriods": "5" 
    "ScalingAdjustmentType": "ChangeInCapacity"
    "ScalingAdjustment": "1"
    }
}

CLI syntax:

$aws gamelift put-scaling-policy
--fleet-id "fleet-2222bbbb-33cc-44dd-55ee-6666ffff77aa"
--name "My_Rule_Policy_1"
--policy-type "RuleBased" 
--metric-name "IdleInstances"
--comparison-operator "LessThanThreshold" 
--threshold "2" 
--evaluation-periods "5" 
--scaling-adjustment-type "ChangeInCapacity"
--scaling-adjustment "1"
                

Sample Response

HTTP/1.1 200 OK
x-amzn-RequestId: b34f8665-EXAMPLE
Content-Type: application/x-amz-json-1.1
Content-Length: 600
Date: Thu, 06 Apr 2017 00:48:07 GMT

{
    "Name": "My_Rule_Policy_1" 
}
                

See Also

For more information about using this API in one of the language-specific AWS SDKs, see the following: