Target tracking scaling policies - Amazon Elastic Container Service
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Target tracking scaling policies

With target tracking scaling policies, you select a metric and set a target value. Amazon ECS Service Auto Scaling creates and manages the CloudWatch alarms that trigger the scaling policy and calculates the scaling adjustment based on the metric and the target value. The scaling policy adds or removes service tasks as required to keep the metric at, or close to, the specified target value. In addition to keeping the metric close to the target value, a target tracking scaling policy also adjusts to the fluctuations in the metric due to a fluctuating load pattern and minimizes rapid fluctuations in the number of tasks running in your service.


Keep the following considerations in mind.

  • A target tracking scaling policy assumes that it should perform scale out when the specified metric is above the target value. You cannot use a target tracking scaling policy to scale out when the specified metric is below the target value.

  • A target tracking scaling policy does not perform scaling when the specified metric has insufficient data. It does not perform scale in because it does not interpret insufficient data as low utilization.

  • You may see gaps between the target value and the actual metric data points. This is because Service Auto Scaling always acts conservatively by rounding up or down when it determines how much capacity to add or remove. This prevents it from adding insufficient capacity or removing too much capacity.

  • To ensure application availability, the service scales out proportionally to the metric as fast as it can, but scales in more gradually.

  • Application Auto Scaling disables scale-in processes while Amazon ECS deployments are in progress. However, scale-out processes continue to occur, unless suspended, during a deployment. For more information, see Service auto scaling and deployments.

  • You can have multiple target tracking scaling policies for an Amazon ECS service, provided that each of them uses a different metric. The intention of Service Auto Scaling is to always prioritize availability, so its behavior differs depending on whether the target tracking policies are ready for scale out or scale in. It will scale out the service if any of the target tracking policies are ready for scale out, but will scale in only if all of the target tracking policies (with the scale-in portion enabled) are ready to scale in.

  • Do not edit or delete the CloudWatch alarms that Service Auto Scaling manages for a target tracking scaling policy. Service Auto Scaling deletes the alarms automatically when you delete the scaling policy.

Tutorial: Service auto scaling with target tracking

The following procedures help you to create an Amazon ECS cluster and a service that uses target tracking to scale out (and in) automatically based on demand.

In this tutorial, you use the Amazon ECS first-run wizard to create a cluster and a service that runs behind an Elastic Load Balancing load balancer. Then you configure a target tracking scaling policy that scales your service automatically based on the current application load as measured by the service's CPU utilization (from the ECS, ClusterName, ServiceName category in CloudWatch).

When the average CPU utilization of your service rises above 75% (meaning that more than 75% of the CPU that is reserved for the service is being used), a scale-out alarm triggers Service Auto Scaling to add another task to your service to help out with the increased load. Conversely, when the average CPU utilization of your service drops below the target utilization for a sustained period of time, a scale-in alarm triggers a decrease in the service's desired count to free up those cluster resources for other tasks and services.


This tutorial assumes that you are using administrator credentials, and that you have an Amazon EC2 key pair in the current region. If you do not have these resources, or your are not sure, you can create them by following the steps in Set up to use Amazon ECS.

Step 1: Create a cluster and a service

Start by creating a cluster and service using the Amazon ECS first-run wizard. The first-run wizard takes care of creating the necessary IAM roles for this tutorial, an Auto Scaling group for your container instances, and a service that runs behind a load balancer. The wizard also makes the clean-up process much easier, because you can delete the entire Amazon CloudFormation stack in one step.

For this tutorial, you create a cluster called service-autoscaling and a service called sample-webapp.

To create your cluster and service

  1. Open the Amazon ECS console first run wizard at

  2. From the navigation bar, choose the US East (N. Virginia) region.

  3. On Step 1: Container and Task, for Container definition, select sample-app.

  4. For Task definition, leave all of the default options and choose Next.

  5. On Step 2: Service, for Load balancer type, choose Application Load Balancer, Next.


    Application Load Balancers do incur costs while they exist in your Amazon resources. For more information, see Elastic Load Balancing Pricing.

  6. On Step 3: Cluster, for Cluster name, enter service-autoscaling and choose Next.

  7. Review your choices and then choose Create.

    You are directed to a Launch Status page that shows the status of your launch and describes each step of the process (this can take a few minutes to complete while your cluster resources are created and populated).

  8. When your cluster and service are created, choose View service.

Step 2: Configure service auto scaling

Now that you have launched a cluster and created a service in that cluster that is running behind a load balancer, you can use Service Auto Scaling by creating a target tracking scaling policy.

To configure basic Service Auto Scaling parameters

  1. On the Service: sample-app-service page, your service configuration should look similar to the image below, although the task definition revision and load balancer name are likely to be different. Choose Update to update your new service.

                                Choose your configuration options
  2. On the Update service page, choose Next step until you get to Step 3: Set Auto Scaling (optional).

  3. For Service Auto Scaling, choose Configure Service Auto Scaling to adjust your service’s desired count.

  4. For Minimum number of tasks, enter 1 for the lower limit of the number of tasks for Service Auto Scaling to use. Your service's desired count is not automatically adjusted below this amount.

  5. For Desired number of tasks, this field is pre-populated with the value that you entered earlier. This value must be between the minimum and maximum number of tasks specified on this page. Leave this value at 1.

  6. For Maximum number of tasks, enter 2 for the upper limit of the number of tasks for Service Auto Scaling to use. Your service's desired count is not automatically adjusted above this amount.

  7. For IAM role for Service Auto Scaling, choose the ecsAutoscaleRole. If this role does not exist, choose Create new role to have the console create it for you.

To configure a target tracking scaling policy for your service

  1. Choose Add scaling policy to configure your scaling policy.

  2. On the Add policy page, update the following fields:

    1. For Scaling policy type, choose Target tracking.

    2. For Policy name, enter TargetTrackingPolicy.

    3. For ECS service metric, choose ECSServiceAverageCPUUtilization.

    4. For Target value, enter 75.

    5. For Scale-out cooldown period, enter 60 seconds. A scale-out activity increases the number of your service's tasks. While the scale-out cooldown period is in effect, the capacity that has been added by the previous scale-out activity that initiated the cooldown is calculated as part of the desired capacity for the next scale out. The intention is to continuously (but not excessively) scale out.

    6. For Scale-in cooldown period, enter 60 seconds. A scale-in activity reduces the number of your service's tasks. The scale-in cooldown period is used to block subsequent scale-in requests until it has expired. The intention is to scale in conservatively to protect your application's availability. However, if another alarm triggers a scale out activity during the cooldown period after a scale-in, Service Auto Scaling scales out your scalable target immediately.

    7. Choose Save.

  3. Choose Next step.

  4. Review all of your choices and then choose Update Service.

  5. When your service status is finished updating, choose View Service.

Step 3: Trigger a scaling activity

After your service is configured with Service Auto Scaling, you can trigger a scaling activity by pushing your service's CPU utilization into the ALARM state. Because the example in this tutorial is a web application that is running behind a load balancer, you can send thousands of HTTP requests to your service (using the ApacheBench utility) to spike the service CPU utilization above the threshold amount. This spike should trigger the alarm, which in turn triggers a scaling activity to add one task to your service.

After the ApacheBench utility finishes the requests, the service CPU utilization should drop below your 75% threshold, triggering a scale-in activity that returns the service's desired count to 1.

To trigger a scaling activity for your service

  1. From your service's main view page in the console, choose the load balancer name to view its details in the Amazon EC2 console. You need the load balancer's DNS name, which should look something like

  2. Use the ApacheBench (ab) utility to make thousands of HTTP requests to your load balancer in a short period of time.


    This command is installed by default on macOS, and it is available for many Linux distributions, as well. For example, you can install ab on Amazon Linux with the following command:

    $ sudo yum install -y httpd24-tools

    Run the following command, substituting your load balancer's DNS name.

    $ ab -n 100000 -c 1000
  3. Open the CloudWatch console at

  4. In the left navigation pane, choose Alarms.

  5. Wait for your ab HTTP requests to trigger the scale-out alarm in the CloudWatch console. You should see your Amazon ECS service scale out and add one task to your service's desired count.

  6. Shortly after your ab HTTP requests complete (between 1 and 2 minutes), your scale in alarm should trigger and the scale in policy reduces your service's desired count back to 1.

Step 4: Next steps

Go to the next step if you would like to delete the basic infrastructure that you just created for this tutorial. Otherwise, you can use this infrastructure as your base and try one or more of the following:

  • To view these scaling activities from the Amazon ECS console, choose the Events tab of the service. When scaling events occur, you see informational messages here. For example:

    Message: Successfully set desired count to 1. Change successfully fulfilled by ecs. Cause: monitor alarm TargetTracking-service/service-autoscaling/sample-webapp-AlarmLow-fcd80aef-5161-4890-aeb4-35dde11ff42c in state ALARM triggered policy TargetTrackingPolicy
  • If you have CloudWatch Container Insights set up and it's collecting Amazon ECS metrics, you can view metric data on the CloudWatch automatic dashboards. For more information, see Introducing Amazon CloudWatch Container Insights for Amazon ECS in the Amazon Compute Blog.

  • Learn how to set up CloudWatch Container Insights. Additional charges apply. For more information, see Amazon ECS CloudWatch Container Insights and Updating cluster settings.

Step 5: Cleaning up

When you have completed this tutorial, you may choose to keep your cluster, Auto Scaling group, load balancer, target tracking scaling policy, and CloudWatch alarms. However, if you are not actively using these resources, you should consider cleaning them up so that your account does not incur unnecessary charges.

To delete your cluster

  1. Open the Amazon ECS console at

  2. In the left navigation pane, choose Clusters.

  3. On the Clusters page, choose the service-autoscaling cluster.

  4. Choose Delete Cluster, Delete. It may take a few minutes for the cluster Amazon CloudFormation stack to finish cleaning up.