Autoscaling in Kubernetes

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1.5 hours
  • 2 Learning Objectives

About this Hands-on Lab

One of the most power features of orchestration tools such as Kubernetes is the ability to automcatically scale resource allocation in response to real-time changes in resource usage. In the context of continuous deployment, this provides a great deal of stability, with less need for human intervention. In this lesson, you will learn the basics of autoscaling in Kubernetes by creating a simple Horizontal Pod Autoscaler which will create and destroy pod replicas in response to CPU utilization.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Install the Kubernetes metrics API in the cluster.

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.
Configure a Horizontal Pod Autoscaler to autoscale the train schedule app.

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:

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!

Additional Resources

Your team has built out a Jenkins Pipeline for deploying the train-schedule app to a Kubernetes cluster. Last week, a celebrity tweeted about how useful the app was. While this was great from a marketing perspective, the resulting large spike in usage caused performance degradation. Usage soon returned to normal levels, but the team wants to be prepared for the next time something like this happens.

You have been asked to implement autoscaling for the app. In the event of another usage spike, the Kubernates cluster will automatically create additional replicas of the app. Then when usage drops again, the cluster will remove the unnecessary replicas.

To do this, you will need to do the following tasks:

Install the Kubernetes metrics API in the cluster:

Configure a Horizontal Pod Autoscaler to autoscale the train schedule app:

  • 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.
  • Define a HorizontalPodAutoscaler in train-schedule-kube.yml to autoscale in response to cpu load.
  • Generate some load on the app to see the autoscaler in action!

The train-schedule image from Docker Hub used for this learning activity includes a special endpoint that does some CPU-intensive calculations: /generate-cpu-load. By hitting this endpoint repeatedly, you can generate CPU load on the app containers to see autoscaling in action. A simple way to generate load is by spinning up a busybox container and running a loop inside it to call that endpoint.

Run a busybox container and open an intractive shell:

kubectl run -i --tty load-generator --image=busybox /bin/sh

Once you're inside the interactive shell, run a command to generate load:

while true; do wget -q -O- http://&ltkubernetes node public ip>:8080/generate-cpu-load; done
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