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The tests will be performed on a cluster consisting of 4 nodes (1 master and 3 worker) with the same flavor. The flavor will also be modified in turn, remaining the same between the VMs in the cluster, multiplying the CPU and RAM by a factor of 2: passing from training medium (2 CPUs and 4GB RAM) to large (4 CPUs and 8GB RAM) and finally xlarge (8 CPUs and 16GB RAM). Finally, the clusters used for the tests are set to "factory settings", ie i.e. they will contain only the starting software of a typical k8s cluster just created. Before we continue, let's familiarize ourselves with a couple of tools suitable for our purposes: Metrics Server and Horizontal Pod Autoscaler.
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Metrics Server deployment will likely not be Ready. If, analyzing the Pod logs, you see the error "unable to fully scrape metrics", then edit the deployment by inserting the flag
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Run and expose php-apache server
To demonstrate HPA we will use a custom docker image based on the php-apache image. Apply the following file, to install a simple PHP web application in the Kubernetes cluster. Then, verify the pods were created.
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We will finish our example by stopping the process, simply deleting the deployment/infinite-calls component or, if you want to reuse it for further testing, scale it to zero replicas. Then, we verify the result state: after a minute or so, re-run the two get commands used earlier. You should get that CPU utilization dropped to 0, and so HPA autoscaled the number of replicas back down to 1.