Installation

The first thing we'll do is install cluster-autoscaler in our cluster. As part of the lab preparation an IAM role has already been created for cluster-autoscaler to call the appropriate AWS APIs.

All that we have left to do is install cluster-autoscaler as a helm chart:

~$helm repo add autoscaler https://kubernetes.github.io/autoscaler

~$helm upgrade --install cluster-autoscaler autoscaler/cluster-autoscaler \

--version "${CLUSTER_AUTOSCALER_CHART_VERSION}" \

--namespace "kube-system" \

--set "autoDiscovery.clusterName=${EKS_CLUSTER_NAME}" \

--set "awsRegion=${AWS_REGION}" \

--set "image.tag=v${CLUSTER_AUTOSCALER_IMAGE_TAG}" \

--set "rbac.serviceAccount.name=cluster-autoscaler-sa" \

--set "rbac.serviceAccount.annotations.eks\\.amazonaws\\.com/role-arn"="$CLUSTER_AUTOSCALER_ROLE" \

--wait

NAME: cluster-autoscaler

LAST DEPLOYED: [...]

NAMESPACE: kube-system

STATUS: deployed

REVISION: 1

TEST SUITE: None

It will be running as a deployment in the kube-system namespace:

~$kubectl get deployment -n kube-system cluster-autoscaler-aws-cluster-autoscaler

NAME                                        READY   UP-TO-DATE   AVAILABLE   AGE

cluster-autoscaler-aws-cluster-autoscaler   1/1     1            1           51s

Now we can move on to modifying our workloads to trigger the provisioning of more compute.