Autoscaling in Kubernetes

To accomplish this, do the following:

• Clone the Kubernetes metrics repo.

git clone https://github.com/kubernetes-incubator/metrics-server.git

• Apply the standard configurations to install the Metrics API.

cd metrics-server/
git checkout ed0663b3b4ddbfab5afea166dfd68c677930d22e
kubectl create -f deploy/1.8+/

• Wait a few seconds for the metrics server pods to start. You can see their status with kubectl get pods -n kube-system.

Check the example-solution branch of the source code repo for an example of the code changes needed in the train-schedule-kube.yml file: https://github.com/linuxacademy/cicd-pipeline-train-schedule-autoscaling/blob/example-solution/train-schedule-kube.yml.

To complete this task, you will need to do the following:

• Create a fork of the source code at https://github.com/linuxacademy/cicd-pipeline-train-schedule-autoscaling.
• Add a CPU resource request in train-schedule-kube.yml for the pods created by the train-schedule deployment. Check the example solution if you need to need to know where to add this.

resources:
  requests:
    cpu: 200m

• Define a HorizontalPodAutoscaler in train-schedule-kube.yml to autoscale in response to CPU load.

---

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: train-schedule
  namespace: default
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: train-schedule-deployment
  minReplicas: 1
  maxReplicas: 4
  metrics:
  - type: Resource
    resource:
      name: cpu
      targetAverageUtilization: 50

• Generate some load on the app to see the autoscaler in action!