Autoscaling an SKS Nodepool

One of the prime features of running your applications on Kubernetes is to be able to scale your cluster without your intervention based on the current workload.

In Kubernetes, one can scale:

• the Pods themselves vertically by raising and lowering resource requests using the Vertical Pod Autoscaler
• the Pods horizontally by raising and lowering the number of Pods in a deployment using the Horizontal Pod Autoscaler (See the test down below for an example)
• the number of nodes by resizing the nodepool based on either node utilization or Pod deployment requirements using the Cluster Autoscaler

Here we will describe the Cluster Autoscaler option.

Prerequisites

As a prerequisite for the following documentation, you need:

• An Exoscale SKS (Pro) Cluster
• Access to your Cluster via kubectl
• Basic Linux knowledge

If you don’t have access to an SKS cluster yet, follow the Quick Start Guide.

Deploying the Cluster Autoscaler

You can find the Kubernetes Autoscaler here and the Exoscale provider in the ./cluster-autoscaler/cloudprovider/exoscale folder.

As described there in the README, you need to first create a secret containing an appropriate API key as well as the zone of your cluster:

export EXOSCALE_API_KEY="EXOxxxxxxxxxxxxxxxxxxxxxxxx"
export EXOSCALE_API_SECRET="xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
export EXOSCALE_ZONE="ch-gva-2"

kubectl -n kube-system create secret generic exoscale-api-credentials \
   --from-literal=api-key="$EXOSCALE_API_KEY" \
   --from-literal=api-secret="$EXOSCALE_API_SECRET" \
   --from-literal=api-zone="$EXOSCALE_ZONE"

Afterwards you can grab the deployment manifests and deploy the Autoscaler:

kubectl apply -f cluster-autoscaler.yaml

Multiple nodepools

In case your cluster has multiple nodepools, you might want to tell the Cluster Autoscaler which nodepool should be scaled up and down. Otherwise a random nodepool will be scaled.

To do this, you need to create a ConfigMap with your nodepools Instance Pool ID (NOT the nodepool ID):

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-autoscaler-priority-expander
  namespace: kube-system
data:
  priorities: |-
    50:
      - 00719c6e-1d06-4053-afea-8926c3431ef7

As well as adding an argument to the autoscaler deployment:

    - --expander=priority

The Cluster Autoscaler will then target this prioritized nodepool instead.

Putting it to the Test

To test everything is working as it should, we deploy a DaemonSet that is very busy but still fit in our current deployment of two nodes:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: stress
  namespace: default
spec:
  replicas: 2
  selector:
    matchLabels:
      run: stress
  template:
    metadata:
      labels:
        run: stress
    spec:
      affinity:
        podAntiAffinity:
          requiredDuringSchedulingIgnoredDuringExecution:
          - labelSelector:
              matchExpressions:
              - key: run
                operator: In
                values:
                - stress
            topologyKey: kubernetes.io/hostname
      containers:
      - image: nixery.dev/stress:latest
        name: stress
        command:
        - stress
        - --cpu
        - "1"
        resources:
          limits:
            cpu: 300m
            memory: 30Mi
          requests:
            cpu: 150m
            memory: 15Mi

Note how we use a podAntiAffinity to ensure these Pods can’t share a node. This reflects real requirements but the node filling up with Pods would result in a similar behaviour.

After this is is deployed, the CPU limit will be consumed in each Pod and the Kubernetes metrics show this:

$ kubectl top pods
NAME                     CPU(cores)   MEMORY(bytes)
stress-d77bdc8db-22dwb   292m         0Mi
stress-d77bdc8db-pd4xn   291m         0Mi

We now use the Horizontal Pod Autoscaler to react on these internal metrics and increase this deployments replicas to 11:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: stress
  namespace: default
spec:
  minReplicas: 1
  maxReplicas: 11
  metrics:
  - resource:
      name: cpu
      target:
        averageUtilization: 50
        type: Utilization
    type: Resource
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: stress

We can see new Pods being spawned and new nodes being added after a short while as the Pods can’t be scheduled onto an existing node:

NAME                  STATUS                     ROLES    AGE    VERSION
pool-0f4cd-fquea      Ready                      <none>   3d6h   v1.23.3
pool-0f4cd-llrsh      NotReady                   <none>   16s    v1.23.3
pool-0f4cd-vhend      NotReady                   <none>   15s    v1.23.3
pool-0f4cd-ygwhj      Ready                      <none>   3d6h   v1.23.3
pool-0f4cd-zknko      NotReady                   <none>   15s    v1.23.3
[...]

You can look into the Cluster Autoscaler logs to see it taking action:

$ kubectl -n kube-system logs deployment/cluster-autoscaler
[...]
I0324 19:50:12.521062       1 scale_up.go:675] Scale-up: setting group 0f4cd2ad-2825-4ae7-aaa2-1fd0f8e0af19 size to 5
I0324 19:50:12.530709       1 log.go:32] exoscale-provider: scaling SKS Nodepool afe7aa21-b4f6-409b-a70b-6fc31d6fada1 to size 5
[...]

Once the new nodes are ready, the Pods will start up on these. When you remove the test deployment, superfluous nodes will be removed after a grace period.

Tips and Tricks

• It can be useful to add --v4 to the list of arguments to get more information in the logs about why the Cluster Autoscaler makes a certain decision.
• Certain Pods can prevent the Autoscaler from removing a node. See the CA FAQ for more on that.
• You can also annotate certain nodes not to be touched by the CA
• Longhorn Users: Note that the Cluster Autoscaler is not fully supported (see)