本文為英文版的機器翻譯版本,如內容有任何歧義或不一致之處,概以英文版為準。
設定 Application Auto Scaling 資源 AWS CloudFormation
本節提供 Application Auto Scaling AWS CloudFormation 模政策的範本範例,以及不同 AWS 資源的排程動作。
重要
當範本中包含 Application Auto Scaling 程式碼片段時,您可能需要針對透過使用範本建立的特定可擴充資源宣告相依性DependsOn 屬性。這會覆寫預設的平行處理原則,並指示 AWS CloudFormation 以指定順序對資源進行作業。否則,擴展組態可能會在完全設定資源前套用。
程式碼片段類別
為 AppStream 叢集建立擴展政策
此程式碼片段說明如何建立策略,並將其套用至使用該AWS::AppStream::Fleet
資AWS::ApplicationAutoScaling::ScalingPolicy
源的資源。資AWS::ApplicationAutoScaling::ScalableTarget
源宣告套用此原則的可擴充目標。Application Auto Scaling 可擴展的機群執行個體數量下限為 1 個執行個體、上限為 20 個執行個體。該政策使機群的平均容量使用率保持在 75%,擴增和縮減的冷卻時間為 300 秒 (5 分鐘)。
它會使用Fn::Join和Ref內建函式,以相同範本中指定的AWS::AppStream::Fleet
資源邏輯名稱來建構ResourceId
屬性。
JSON
{ "Resources" : { "ScalableTarget" : { "Type" : "AWS::ApplicationAutoScaling::ScalableTarget", "Properties" : { "MaxCapacity" : 20, "MinCapacity" : 1, "RoleARN" : { "Fn::Sub" : "arn:aws:iam::${AWS::AccountId}:role/aws-service-role/appstream.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_AppStreamFleet" }, "ServiceNamespace" : "appstream", "ScalableDimension" : "appstream:fleet:DesiredCapacity", "ResourceId" : { "Fn::Join" : [ "/", [ "fleet", { "Ref" : "
logicalName
" } ] ] } } }, "ScalingPolicyAppStreamFleet" : { "Type" : "AWS::ApplicationAutoScaling::ScalingPolicy", "Properties" : { "PolicyName" : { "Fn::Sub" : "${AWS::StackName}-target-tracking-cpu75" }, "PolicyType" : "TargetTrackingScaling", "ServiceNamespace" : "appstream", "ScalableDimension" : "appstream:fleet:DesiredCapacity", "ResourceId" : { "Fn::Join" : [ "/", [ "fleet", { "Ref" : "logicalName
" } ] ] }, "TargetTrackingScalingPolicyConfiguration" : { "TargetValue" : 75, "PredefinedMetricSpecification" : { "PredefinedMetricType" : "AppStreamAverageCapacityUtilization" }, "ScaleInCooldown" : 300, "ScaleOutCooldown" : 300 } } } } }
YAML
--- Resources: ScalableTarget: Type: AWS::ApplicationAutoScaling::ScalableTarget Properties: MaxCapacity: 20 MinCapacity: 1 RoleARN: Fn::Sub: 'arn:aws:iam::${AWS::AccountId}:role/aws-service-role/appstream.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_AppStreamFleet' ServiceNamespace: appstream ScalableDimension: appstream:fleet:DesiredCapacity ResourceId: !Join - / - - fleet - !Ref
logicalName
ScalingPolicyAppStreamFleet: Type: AWS::ApplicationAutoScaling::ScalingPolicy Properties: PolicyName: !Sub ${AWS::StackName}-target-tracking-cpu75 PolicyType: TargetTrackingScaling ServiceNamespace: appstream ScalableDimension: appstream:fleet:DesiredCapacity ResourceId: !Join - / - - fleet - !ReflogicalName
TargetTrackingScalingPolicyConfiguration: TargetValue: 75 PredefinedMetricSpecification: PredefinedMetricType: AppStreamAverageCapacityUtilization ScaleInCooldown: 300 ScaleOutCooldown: 300
為 Aurora 資料庫叢集建立擴展政策
在此程式碼片段中,您可以註冊AWS::RDS::DBCluster
資源。資AWS::ApplicationAutoScaling::ScalableTarget
源指示應動態擴展資料庫叢集,使其具有從一到八個 Aurora 複本。您也可以使用AWS::ApplicationAutoScaling::ScalingPolicy
資源將目標追蹤擴展政策套用至叢集。
在此組態中,RDSReaderAverageCPUUtilization
預先定義的指標用於根據該 Aurora 資料庫叢集中所有 Aurora 複本的平均使用CPU率 40% 來調整 Aurora 資料庫叢集。這個組態分別提供了 10 分鐘的規模縮減冷卻時間,和 5 分鐘的橫向擴展冷卻時間。
此範例會使用Fn::Sub內建函數,以相同範本中指定之AWS::RDS::DBCluster
資源的邏輯名稱來建構ResourceId
屬性。
JSON
{ "Resources" : { "ScalableTarget" : { "Type" : "AWS::ApplicationAutoScaling::ScalableTarget", "Properties" : { "MaxCapacity" : 8, "MinCapacity" : 1, "RoleARN" : { "Fn::Sub" : "arn:aws:iam::${AWS::AccountId}:role/aws-service-role/rds.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_RDSCluster" }, "ServiceNamespace" : "rds", "ScalableDimension" : "rds:cluster:ReadReplicaCount", "ResourceId" : { "Fn::Sub" : "cluster:${
logicalName
}" } } }, "ScalingPolicyDBCluster" : { "Type" : "AWS::ApplicationAutoScaling::ScalingPolicy", "Properties" : { "PolicyName" : { "Fn::Sub" : "${AWS::StackName}-target-tracking-cpu40" }, "PolicyType" : "TargetTrackingScaling", "ServiceNamespace" : "rds", "ScalableDimension" : "rds:cluster:ReadReplicaCount", "ResourceId" : { "Fn::Sub" : "cluster:${logicalName
}" }, "TargetTrackingScalingPolicyConfiguration" : { "TargetValue" : 40, "PredefinedMetricSpecification" : { "PredefinedMetricType" : "RDSReaderAverageCPUUtilization" }, "ScaleInCooldown" : 600, "ScaleOutCooldown" : 300 } } } } }
YAML
--- Resources: ScalableTarget: Type: AWS::ApplicationAutoScaling::ScalableTarget Properties: MaxCapacity: 8 MinCapacity: 1 RoleARN: Fn::Sub: 'arn:aws:iam::${AWS::AccountId}:role/aws-service-role/rds.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_RDSCluster' ServiceNamespace: rds ScalableDimension: rds:cluster:ReadReplicaCount ResourceId: !Sub cluster:${
logicalName
} ScalingPolicyDBCluster: Type: AWS::ApplicationAutoScaling::ScalingPolicy Properties: PolicyName: !Sub ${AWS::StackName}-target-tracking-cpu40 PolicyType: TargetTrackingScaling ServiceNamespace: rds ScalableDimension: rds:cluster:ReadReplicaCount ResourceId: !Sub cluster:${logicalName
} TargetTrackingScalingPolicyConfiguration: TargetValue: 40 PredefinedMetricSpecification: PredefinedMetricType: RDSReaderAverageCPUUtilization ScaleInCooldown: 600 ScaleOutCooldown: 300
為 DynamoDB 表格建立擴展政策
此程式碼片段說明如何使用該策略類型建立TargetTrackingScaling
策略,並將其套用至使用該AWS::DynamoDB::Table
資AWS::ApplicationAutoScaling::ScalingPolicy
源的資源。資AWS::ApplicationAutoScaling::ScalableTarget
源宣告套用此原則的可擴充目標,其中至少有五個寫入容量單位,最多 15 個。此擴展政策會根據 DynamoDBWriteCapacityUtilization
預先定義指標擴展資料表的寫入容量輸送量,以將目標使用率維持在 50%。
它會使用Fn::Join和Ref內建函式,以相同範本中指定的AWS::DynamoDB::Table
資源邏輯名稱來建構ResourceId
屬性。
注意
如需有關如何為 DynamoDB 資源建立 AWS CloudFormation 範本的詳細資訊,請參閱部落格文章如何在資料庫部落格上使用 AWS CloudFormation Amazon DynamoDB 表格和索引設定 auto 擴展
JSON
{ "Resources" : { "WriteCapacityScalableTarget" : { "Type" : "AWS::ApplicationAutoScaling::ScalableTarget", "Properties" : { "MaxCapacity" : 15, "MinCapacity" : 5, "RoleARN" : { "Fn::Sub" : "arn:aws:iam::${AWS::AccountId}:role/aws-service-role/dynamodb.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_DynamoDBTable" }, "ServiceNamespace" : "dynamodb", "ScalableDimension" : "dynamodb:table:WriteCapacityUnits", "ResourceId" : { "Fn::Join" : [ "/", [ "table", { "Ref" : "
logicalName
" } ] ] } } }, "WriteScalingPolicy" : { "Type" : "AWS::ApplicationAutoScaling::ScalingPolicy", "Properties" : { "PolicyName" : "WriteScalingPolicy", "PolicyType" : "TargetTrackingScaling", "ScalingTargetId" : { "Ref" : "WriteCapacityScalableTarget" }, "TargetTrackingScalingPolicyConfiguration" : { "TargetValue" : 50.0, "ScaleInCooldown" : 60, "ScaleOutCooldown" : 60, "PredefinedMetricSpecification" : { "PredefinedMetricType" : "DynamoDBWriteCapacityUtilization" } } } } } }
YAML
--- Resources: WriteCapacityScalableTarget: Type: AWS::ApplicationAutoScaling::ScalableTarget Properties: MaxCapacity: 15 MinCapacity: 5 RoleARN: Fn::Sub: 'arn:aws:iam::${AWS::AccountId}:role/aws-service-role/dynamodb.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_DynamoDBTable' ServiceNamespace: dynamodb ScalableDimension: dynamodb:table:WriteCapacityUnits ResourceId: !Join - / - - table - !Ref
logicalName
WriteScalingPolicy: Type: AWS::ApplicationAutoScaling::ScalingPolicy Properties: PolicyName: WriteScalingPolicy PolicyType: TargetTrackingScaling ScalingTargetId: !Ref WriteCapacityScalableTarget TargetTrackingScalingPolicyConfiguration: TargetValue: 50.0 ScaleInCooldown: 60 ScaleOutCooldown: 60 PredefinedMetricSpecification: PredefinedMetricType: DynamoDBWriteCapacityUtilization
建立 Amazon ECS 服務的擴展政策 (指標:平均值CPU和記憶體)
此程式碼片段說明如何建立策略,並將其套用至使用該AWS::ECS::Service
資AWS::ApplicationAutoScaling::ScalingPolicy
源的資源。資AWS::ApplicationAutoScaling::ScalableTarget
源宣告套用此原則的可擴充目標。Application Auto Scaling 可擴展的任務數量下限為 1 個任務、上限為 6 個任務。
這會建立兩個具有 TargetTrackingScaling
政策類型的擴展政策。這些原則是用來根據ECS服務的平均值CPU和記憶體使用量來調整服務的規模。它會使用Fn::Join和Ref內建函式,以相同範本中指定的 AWS::ECS::Cluster
(myContainerCluster
) 和 AWS::ECS::Service
(myService
) 資源的邏輯名稱來建構ResourceId
屬性。
JSON
{ "Resources" : { "ECSScalableTarget" : { "Type" : "AWS::ApplicationAutoScaling::ScalableTarget", "Properties" : { "MaxCapacity" : "6", "MinCapacity" : "1", "RoleARN" : { "Fn::Sub" : "arn:aws:iam::${AWS::AccountId}:role/aws-service-role/ecs.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_ECSService" }, "ServiceNamespace" : "ecs", "ScalableDimension" : "ecs:service:DesiredCount", "ResourceId" : { "Fn::Join" : [ "/", [ "service", { "Ref" : "
myContainerCluster
" }, { "Fn::GetAtt" : [ "myService
", "Name" ] } ] ] } } }, "ServiceScalingPolicyCPU" : { "Type" : "AWS::ApplicationAutoScaling::ScalingPolicy", "Properties" : { "PolicyName" : { "Fn::Sub" : "${AWS::StackName}-target-tracking-cpu70" }, "PolicyType" : "TargetTrackingScaling", "ScalingTargetId" : { "Ref" : "ECSScalableTarget" }, "TargetTrackingScalingPolicyConfiguration" : { "TargetValue" : 70.0, "ScaleInCooldown" : 180, "ScaleOutCooldown" : 60, "PredefinedMetricSpecification" : { "PredefinedMetricType" : "ECSServiceAverageCPUUtilization" } } } }, "ServiceScalingPolicyMem" : { "Type" : "AWS::ApplicationAutoScaling::ScalingPolicy", "Properties" : { "PolicyName" : { "Fn::Sub" : "${AWS::StackName}-target-tracking-mem90" }, "PolicyType" : "TargetTrackingScaling", "ScalingTargetId" : { "Ref" : "ECSScalableTarget" }, "TargetTrackingScalingPolicyConfiguration" : { "TargetValue" : 90.0, "ScaleInCooldown" : 180, "ScaleOutCooldown" : 60, "PredefinedMetricSpecification" : { "PredefinedMetricType" : "ECSServiceAverageMemoryUtilization" } } } } } }
YAML
--- Resources: ECSScalableTarget: Type: AWS::ApplicationAutoScaling::ScalableTarget Properties: MaxCapacity: 6 MinCapacity: 1 RoleARN: Fn::Sub: 'arn:aws:iam::${AWS::AccountId}:role/aws-service-role/ecs.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_ECSService' ServiceNamespace: ecs ScalableDimension: 'ecs:service:DesiredCount' ResourceId: !Join - / - - service - !Ref
myContainerCluster
- !GetAttmyService
.Name ServiceScalingPolicyCPU: Type: AWS::ApplicationAutoScaling::ScalingPolicy Properties: PolicyName: !Sub ${AWS::StackName}-target-tracking-cpu70 PolicyType: TargetTrackingScaling ScalingTargetId: !Ref ECSScalableTarget TargetTrackingScalingPolicyConfiguration: TargetValue: 70.0 ScaleInCooldown: 180 ScaleOutCooldown: 60 PredefinedMetricSpecification: PredefinedMetricType: ECSServiceAverageCPUUtilization ServiceScalingPolicyMem: Type: AWS::ApplicationAutoScaling::ScalingPolicy Properties: PolicyName: !Sub ${AWS::StackName}-target-tracking-mem90 PolicyType: TargetTrackingScaling ScalingTargetId: !Ref ECSScalableTarget TargetTrackingScalingPolicyConfiguration: TargetValue: 90.0 ScaleInCooldown: 180 ScaleOutCooldown: 60 PredefinedMetricSpecification: PredefinedMetricType: ECSServiceAverageMemoryUtilization
建立 Amazon ECS 服務的擴展政策 (指標:每個目標的平均請求計數)
下列範例會將含有ALBRequestCountPerTarget
預先定義之量度的目標追蹤資源調度政策套用至ECS服務。當每個目標的要求計數 (每分鐘) 超過目標值時,此原則可用來為ECS服務新增容量。因為 DisableScaleIn
的值設為 true
,所以目標追蹤政策不會從可擴展的目標移除容量。
它會使用Fn::Join和Fn::GetAtt內建函式,以相同範本中指定的 AWS::ElasticLoadBalancingV2::LoadBalancer
(myLoadBalancer
) 和 AWS::ElasticLoadBalancingV2::TargetGroup
(myTargetGroup
) 資源的邏輯名稱來建構ResourceLabel
屬性。
可擴展目標的 MaxCapacity
和 MinCapacity
屬性和擴展政策的 TargetValue
屬性會參考建立或更新堆疊時傳遞至範本的參數值。
JSON
{ "Resources" : { "ECSScalableTarget" : { "Type" : "AWS::ApplicationAutoScaling::ScalableTarget", "Properties" : { "MaxCapacity" : { "Ref" : "MaxCount" }, "MinCapacity" : { "Ref" : "MinCount" }, "RoleARN" : { "Fn::Sub" : "arn:aws:iam::${AWS::AccountId}:role/aws-service-role/ecs.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_ECSService" }, "ServiceNamespace" : "ecs", "ScalableDimension" : "ecs:service:DesiredCount", "ResourceId" : { "Fn::Join" : [ "/", [ "service", { "Ref" : "
myContainerCluster
" }, { "Fn::GetAtt" : [ "myService
", "Name" ] } ] ] } } }, "ServiceScalingPolicyALB" : { "Type" : "AWS::ApplicationAutoScaling::ScalingPolicy", "Properties" : { "PolicyName" : "alb-requests-per-target-per-minute", "PolicyType" : "TargetTrackingScaling", "ScalingTargetId" : { "Ref" : "ECSScalableTarget" }, "TargetTrackingScalingPolicyConfiguration" : { "TargetValue" : { "Ref" : "ALBPolicyTargetValue" }, "ScaleInCooldown" : 180, "ScaleOutCooldown" : 30, "DisableScaleIn" : true, "PredefinedMetricSpecification" : { "PredefinedMetricType" : "ALBRequestCountPerTarget", "ResourceLabel" : { "Fn::Join" : [ "/", [ { "Fn::GetAtt" : [ "myLoadBalancer
", "LoadBalancerFullName" ] }, { "Fn::GetAtt" : [ "myTargetGroup
", "TargetGroupFullName" ] } ] ] } } } } } } }
YAML
--- Resources: ECSScalableTarget: Type: AWS::ApplicationAutoScaling::ScalableTarget Properties: MaxCapacity: !Ref MaxCount MinCapacity: !Ref MinCount RoleARN: Fn::Sub: 'arn:aws:iam::${AWS::AccountId}:role/aws-service-role/ecs.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_ECSService' ServiceNamespace: ecs ScalableDimension: 'ecs:service:DesiredCount' ResourceId: !Join - / - - service - !Ref
myContainerCluster
- !GetAttmyService
.Name ServiceScalingPolicyALB: Type: AWS::ApplicationAutoScaling::ScalingPolicy Properties: PolicyName: alb-requests-per-target-per-minute PolicyType: TargetTrackingScaling ScalingTargetId: !Ref ECSScalableTarget TargetTrackingScalingPolicyConfiguration: TargetValue: !Ref ALBPolicyTargetValue ScaleInCooldown: 180 ScaleOutCooldown: 30 DisableScaleIn: true PredefinedMetricSpecification: PredefinedMetricType: ALBRequestCountPerTarget ResourceLabel: !Join - '/' - - !GetAttmyLoadBalancer
.LoadBalancerFullName - !GetAttmyTargetGroup
.TargetGroupFullName
使用 Lambda 函數的 Cron 運算式建立排程動作
此程式碼片段會為BLUE
使用AWS::ApplicationAutoScaling::ScalableTarget
資源命名的函數 alias (AWS::Lambda::Alias
) 註冊佈建的並行。還建立排程動作,並使用 Cron 運算式設定重複排程。週期性排程的時區為UTC。
它會使用RoleARN
屬性中的Fn::Join和Ref內建函數來指定服務連結角色ARN的。它會使用Fn::Sub內建函式來建構具有在相同範本中指定之AWS::Lambda::Function
或AWS::Serverless::Function
資源邏輯名稱的ResourceId
屬性。
注意
您無法在指向未發佈版本 () $LATEST
的別名上配置佈建的並行。
如需如何為 Lambda 資源建立 AWS CloudFormation 範本的詳細資訊,請參閱 AWS 計算部落格上的部落格文章針對週期性尖峰使用量排定 AWS Lambda 佈
JSON
{ "ScalableTarget" : { "Type" : "AWS::ApplicationAutoScaling::ScalableTarget", "Properties" : { "MaxCapacity" : 250, "MinCapacity" : 0, "RoleARN" : { "Fn::Join" : [ ":", [ "arn:aws:iam:", { "Ref" : "AWS::AccountId" }, "role/aws-service-role/lambda.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_LambdaConcurrency" ] ] }, "ServiceNamespace" : "lambda", "ScalableDimension" : "lambda:function:ProvisionedConcurrency", "ResourceId" : { "Fn::Sub" : "function:${
logicalName
}:BLUE" }, "ScheduledActions" : [ { "ScalableTargetAction" : { "MinCapacity" : "250" }, "ScheduledActionName" : "my-scale-out-scheduled-action", "Schedule" : "cron(0 18 * * ? *)", "EndTime" : "2022-12-31T12:00:00.000Z" } ] } } }
YAML
ScalableTarget: Type: AWS::ApplicationAutoScaling::ScalableTarget Properties: MaxCapacity: 250 MinCapacity: 0 RoleARN: !Join - ':' - - 'arn:aws:iam:' - !Ref 'AWS::AccountId' - role/aws-service-role/lambda.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_LambdaConcurrency ServiceNamespace: lambda ScalableDimension: lambda:function:ProvisionedConcurrency ResourceId: !Sub function:${
logicalName
}:BLUE ScheduledActions: - ScalableTargetAction: MinCapacity: 250 ScheduledActionName: my-scale-out-scheduled-action Schedule: 'cron(0 18 * * ? *)' EndTime: '2022-12-31T12:00:00.000Z'
使用 Spot 叢集的at
運算式建立排程動作
此程式碼片段說明如何為使用該資源的AWS::EC2::SpotFleet
資源建立兩個排程動作,這些動作僅發生一次。AWS::ApplicationAutoScaling::ScalableTarget
每個一次性排程動作的時區為UTC。
它會使用Fn::Join和Ref內建函式,以相同範本中指定的AWS::EC2::SpotFleet
資源邏輯名稱來建構ResourceId
屬性。
注意
Spot 機群請求必須具有 maintain
的請求類型。一次性請求或 Spot 區塊不支援自動擴展功能。
JSON
{ "Resources" : { "SpotFleetScalableTarget" : { "Type" : "AWS::ApplicationAutoScaling::ScalableTarget", "Properties" : { "MaxCapacity" : 0, "MinCapacity" : 0, "RoleARN" : { "Fn::Sub" : "arn:aws:iam::${AWS::AccountId}:role/aws-service-role/ec2.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_EC2SpotFleetRequest" }, "ServiceNamespace" : "ec2", "ScalableDimension" : "ec2:spot-fleet-request:TargetCapacity", "ResourceId" : { "Fn::Join" : [ "/", [ "spot-fleet-request", { "Ref" : "
logicalName
" } ] ] }, "ScheduledActions" : [ { "ScalableTargetAction" : { "MaxCapacity" : "10", "MinCapacity" : "10" }, "ScheduledActionName" : "my-scale-out-scheduled-action", "Schedule" : "at(2022-05-20T13:00:00)" }, { "ScalableTargetAction" : { "MaxCapacity" : "0", "MinCapacity" : "0" }, "ScheduledActionName" : "my-scale-in-scheduled-action", "Schedule" : "at(2022-05-20T21:00:00)" } ] } } } }
YAML
--- Resources: SpotFleetScalableTarget: Type: AWS::ApplicationAutoScaling::ScalableTarget Properties: MaxCapacity: 0 MinCapacity: 0 RoleARN: Fn::Sub: 'arn:aws:iam::${AWS::AccountId}:role/aws-service-role/ec2.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_EC2SpotFleetRequest' ServiceNamespace: ec2 ScalableDimension: 'ec2:spot-fleet-request:TargetCapacity' ResourceId: !Join - / - - spot-fleet-request - !Ref
logicalName
ScheduledActions: - ScalableTargetAction: MaxCapacity: 10 MinCapacity: 10 ScheduledActionName: my-scale-out-scheduled-action Schedule: 'at(2022-05-20T13:00:00)' - ScalableTargetAction: MaxCapacity: 0 MinCapacity: 0 ScheduledActionName: my-scale-in-scheduled-action Schedule: 'at(2022-05-20T21:00:00)'