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Cluster Autoscaler on AWS

On AWS, Cluster Autoscaler utilizes Amazon EC2 Auto Scaling Groups to manage node groups. Cluster Autoscaler typically runs as a Deployment in your cluster.


Cluster Autoscaler requires Kubernetes v1.3.0 or greater.


Cluster Autoscaler requires the ability to examine and modify EC2 Auto Scaling Groups. We recommend using IAM roles for Service Accounts to associate the Service Account that the Cluster Autoscaler Deployment runs as with an IAM role that is able to perform these functions. If you are unable to use IAM Roles for Service Accounts, you may associate an IAM service role with the EC2 instance on which the Cluster Autoscaler pod runs.

IAM Policy

The following policy provides the minimum privileges necessary for Cluster Autoscaler to run:

    "Version": "2012-10-17",
    "Statement": [
            "Effect": "Allow",
            "Action": [
            "Resource": ["*"]

If you'd like Cluster Autoscaler to automatically discover EC2 Auto Scaling Groups (recommended), add autoscaling:DescribeTags to the Action list. Also add autoscaling:DescribeLaunchConfigurations (if you created your ASG using a Launch Configuration) and/or ec2:DescribeLaunchTemplateVersions (if you created your ASG using a Launch Template) to the Action list.

If you prefer, you can restrict the target resources for the autoscaling actions by specifying Auto Scaling Group ARNs in the Resource list of the policy. More information can be found here.

Using OIDC Federated Authentication

OIDC federated authentication allows your service to assume an IAM role and interact with AWS services without having to store credentials as environment variables. For an example of how to use AWS IAM OIDC with the Cluster Autoscaler please see here.

Using AWS Credentials

NOTE The following is not recommended for Kubernetes clusters running on AWS. If you are using Amazon EKS, consider using IAM roles for Service Accounts instead.

For on-premise clusters, you may create an IAM user subject to the above policy and provide the IAM credentials as environment variables in the Cluster Autoscaler deployment manifest. Cluster Autoscaler will use these credentials to authenticate and authorize itself.

apiVersion: v1
kind: Secret
  name: aws-secret
type: Opaque
  aws_access_key_id: BASE64_OF_YOUR_AWS_ACCESS_KEY_ID
  aws_secret_access_key: BASE64_OF_YOUR_AWS_SECRET_ACCESS_KEY

Please refer to the relevant Kubernetes documentation for creating a secret manually.

      name: aws-secret
      key: aws_access_key_id
      name: aws-secret
      key: aws_secret_access_key
- name: AWS_REGION

Auto-Discovery Setup

Auto-Discovery Setup is the preferred method to configure Cluster Autoscaler.

To enable this, provide the --node-group-auto-discovery flag as an argument whose value is a list of tag keys that should be looked for. For example,,<cluster-name> will find the ASGs where those tag keys exist. It does not matter what value the tags have.

Example deployment:

kubectl apply -f examples/cluster-autoscaler-autodiscover.yaml

Cluster Autoscaler will respect the minimum and maximum values of each Auto Scaling Group. It will only adjust the desired value.

Each Auto Scaling Group should be composed of instance types that provide approximately equal capacity. For example, ASG "xlarge" could be composed of m5a.xlarge, m4.xlarge, m5.xlarge, and m5d.xlarge instance types, because each of those provide 4 vCPUs and 16GiB RAM. Separately, ASG "2xlarge" could be composed of m5a.2xlarge, m4.2xlarge, m5.2xlarge, and m5d.2xlarge instance types, because each of those provide 8 vCPUs and 32GiB RAM.

Cluster Autoscaler will attempt to determine the CPU, memory, and GPU resources provided by an Auto Scaling Group based on the instance type specified in its Launch Configuration or Launch Template. It will also examine any overrides provided in an ASG's Mixed Instances Policy. If any such overrides are found, only the first instance type found will be used. See Using Mixed Instances Policies and Spot Instances for details.

From version 1.14, Cluster Autoscaler can also determine the resources provided by each Auto Scaling Group via tags. The tag is of the format<resource-name>. <resource-name> is the name of the resource, such as ephemeral-storage. The value of each tag specifies the amount of resource provided. The units are identical to the units used in the resources field of a Pod specification.

Example tags:

  • 100G

You may also provide additional hints to Cluster Autoscaler that the nodes will be labeled or tainted when they join the cluster, such as:

  • bar
  • NoSchedule

NOTE: It is your responsibility to ensure such labels and/or taints are applied via the node's kubelet configuration at startup.


  • It is recommended to use a second tag like<cluster-name> when is used across many clusters to prevent ASGs from different clusters recognized as the node groups.
  • To prevent conflicts, do not provide a --nodes argument if --node-group-auto-discovery is specified.
  • Be sure to add autoscaling:DescribeLaunchConfigurations or ec2:DescribeLaunchTemplateVersions to the Action list of the IAM Policy used by Cluster Autoscaler, depending on whether your ASG utilizes Launch Configurations or Launch Templates.
  • If Cluster Autoscaler adds a node to the cluster, and the node has taints applied when it joins the cluster that Cluster Autoscaler was unaware of (because the tag wasn't supplied), this can lead to significant confusion and misbehavior.

Special note on GPU instances

The device plugin on nodes that provides GPU resources can take some time to advertise the GPU resource to the cluster. This may cause Cluster Autoscaler to unnecessarily scale out multiple times.

To avoid this, you can configure kubelet on your GPU nodes to label the node before it joins the cluster by passing it the --node-labels flag. The label format is as follows:

  • Cluster Autoscaler < 1.15:<gpu-type>
  • Cluster Autoscaler >= 1.15:<gpu-type>

<gpu-type> varies by instance type. On P2 instances, for example, the value is nvidia-tesla-k80.

Manual configuration

Cluster Autoscaler can also be configured manually if you wish by passing the --nodes argument at startup. The format of the argument is --nodes=<min>:<max>:<asg-name>, where <min> is the minimum number of nodes, <max> is the maximum number of nodes, and <asg-name> is the Auto Scaling Group name.

You can pass multiple --nodes arguments if you have multiple Auto Scaling Groups you want Cluster Autoscaler to use.


  • Both <min> and <max> must be within the range of the minimum and maximum instance counts specified by the Auto Scaling group.
  • When manual configuration is used, all Auto Scaling groups must use EC2 instance types that provide equal CPU and memory capacity.


One ASG Setup (min: 1, max: 10, ASG Name: k8s-worker-asg-1)

kubectl apply -f examples/cluster-autoscaler-one-asg.yaml

Multiple ASG Setup

kubectl apply -f examples/cluster-autoscaler-multi-asg.yaml

Control Plane (previously referred to as master) Node Setup

NOTE: This setup is not compatible with Amazon EKS.

To run a CA pod on a control plane node the CA deployment should tolerate the master taint and nodeSelector should be used to schedule the pods on a control plane node. Please replace {{ node_asg_min }}, {{ node_asg_max }} and {{ name }} with your ASG setting in the yaml file.

kubectl apply -f examples/cluster-autoscaler-run-on-control-plane.yaml

Using Mixed Instances Policies and Spot Instances

NOTE: The minimum version of cluster autoscaler to support MixedInstancePolicy is v1.14.x.

If your workloads can tolerate interruption, consider taking advantage of Spot Instances for a lower price point. To enable diversity among On Demand and Spot Instances, as well as specify multiple EC2 instance types in order to tap into multiple Spot capacity pools, use a mixed instances policy on your ASG. Note that the instance types should have the same amount of RAM and number of CPU cores, since this is fundamental to CA's scaling calculations. Using mismatched instances types can produce unintended results. See an example below.

Additionally, there are other factors which affect scaling, such as node labels. If you are currently using nodeSelector with the label, you will need to apply a common propagating label to the ASG and use that instead, since the instance-type label can no longer be relied upon. One may also use auto-generated tags such as aws:cloudformation:stack-name for this purpose. Node affinity and anti-affinity are not affected in the same way, since these selectors natively accept multiple values; one must add all the configured instances types to the list of values, for example:

        - matchExpressions:
          - key:
            operator: In
            - r5.2xlarge
            - r5d.2xlarge
            - r5a.2xlarge
            - r5ad.2xlarge
            - r5n.2xlarge
            - r5dn.2xlarge
            - r4.2xlarge
            - i3.2xlarge

Example usage:

  • Create a Launch Template (LT) with an instance type, for example, r5.2xlarge. Consider this the 'base' instance type. Do not define any spot purchase options here.
  • Create an ASG with a MixedInstancesPolicy that refers to the newly-created LT.
  • Set LaunchTemplateOverrides to include the 'base' instance type r5.2xlarge and suitable alternatives, e.g. r5d.2xlarge, i3.2xlarge, r5a.2xlarge and r5ad.2xlarge. Differing processor types and speeds should be evaluated depending on your use-case(s).
  • Set InstancesDistribution according to your needs.
  • See Allocation Strategies for information about how the ASG fulfills capacity from the specified instance types. It is recommended to use the capacity-optimized allocation strategy, which will automatically launch Spot Instances into the most available pools by looking at real-time capacity data and.
  • For the same workload or for the generic capacity in your cluster, you can also create more node groups with a vCPU/Mem ratio that is a good fit for your workloads, but from different instance sizes. For example: Node group 1: m5.xlarge, m5a.xlarge, m5d.xlarge, m5ad.xlarge, m4.xlarge. Node group 2: m5.2xlarge, m5a.2xlarge, m5d.2xlarge, m5ad.2xlarge, m4.2xlarge. This approach increases the chance of achieving your desired scale at the lowest cost by tapping into many Spot capacity pools.

See CloudFormation example here.

Use Static Instance List

The set of the latest supported EC2 instance types will be fetched by the CA at run time. You can find all the available instance types in the CA logs. If your network access is restricted such that fetching this set is infeasible, you can specify the command-line flag --aws-use-static-instance-list=true to switch the CA back to its original use of a statically defined set.

To refresh static list, please run go run ec2_instance_types/gen.go under cluster-autoscaler/cloudprovider/aws/ and update staticListLastUpdateTime in aws_util.go

Common Notes and Gotchas:

  • The /etc/ssl/certs/ca-bundle.crt should exist by default on ec2 instance in your EKS cluster. If you use other cluster privision tools like kops with different operating systems other than Amazon Linux 2, please use /etc/ssl/certs/ca-certificates.crt or correct path on your host instead for the volume hostPath in your cluster autoscaler manifest.
  • If you’re using Persistent Volumes, your deployment needs to run in the same AZ as where the EBS volume is, otherwise the pod scheduling could fail if it is scheduled in a different AZ and cannot find the EBS volume. To overcome this, either use a single AZ ASG for this use case, or an ASG-per-AZ while enabling --balance-similar-node-groups. Alternately, and depending on your use-case, you might be able to switch from using EBS to using shared storage that is available across AZs (for each pod in its respective AZ). Consider AWS services like Amazon EFS or Amazon FSx for Lustre.
  • On creation time, the ASG will have the AZRebalance process enabled, which means it will actively work to balance the number of instances between AZs, and possibly terminate instances. If your applications could be impacted from sudden termination, you can either suspend the AZRebalance feature, or use a tool for automatic draining upon ASG scale-in such as the k8s-node-drainer. The AWS Node Termination Handler will also support this use-case in the future.
  • By default, cluster autoscaler will not terminate nodes running pods in the kube-system namespace. You can override this default behaviour by passing in the --skip-nodes-with-system-pods=false flag.
  • By default, cluster autoscaler will wait 10 minutes between scale down operations, you can adjust this using the --scale-down-delay-after-add, --scale-down-delay-after-delete, and --scale-down-delay-after-failure flag. E.g. --scale-down-delay-after-add=5m to decrease the scale down delay to 5 minutes after a node has been added.
  • If you're running multiple ASGs, the --expander flag supports three options: random, most-pods and least-waste. random will expand a random ASG on scale up. most-pods will scale up the ASG that will schedule the most amount of pods. least-waste will expand the ASG that will waste the least amount of CPU/MEM resources. In the event of a tie, cluster autoscaler will fall back to random.
  • If you're managing your own kubelets, they need to be started with the --provider-id flag. The provider id has the format aws:///<availability-zone>/<instance-id>, e.g. aws:///us-east-1a/i-01234abcdef.
  • If you want to use regional STS endpoints (e.g. when using VPC endpoint for STS) the env AWS_STS_REGIONAL_ENDPOINTS=regional should be set.