Kubernetes Part V - HPA, Prometheus and Metrics Server

Now that I have a cluster built, I am trying to implement some of the more advanced functions of Kubernetes, such as Scaling.

I'm familiar with how Scaling works in OpenStack with an ETSI MANO Orchestrator (OpenBaton). Now, I would like to see how Kubernetes implements this kind of concept.

A previous engineer made reference to a project in GitHub, which is a Kubernetes Horizontal Pod Autoscaler, with Prometheus custom metrics.
So, I will start with that.  The  link in GitHub to the project I am referring to here is:
https://github.com/stefanprodan/k8s-prom-hpa

This GitHub site, unlike many, has some pretty extensive documentation about metrics in Kubernetes.

What this site covers, is the following steps:

1. Create a Metrics Server (Basic)

This allows you to "do things" (i.e. Scale Horizontally) based on "policies" (like CPU Usage, Memory Usage, File System Usage, etc).

With the "basic" Metrics Server, it comes with the most commonly used metrics. From what I can gather, nodes and pods are queried (polled) for the common metrics by the HPA (Horizontal Pod Autoscaler), which in turn uses a Metrics API to send the metrics to the Metrics Server. Kubelet then pulls the metrics from the Metrics Server.

2. Creating a Custom Metrics Server

For Custom Metrics, applications and services send their metrics via a Custom Metrics API to a Prometheus database.  These then get sent to Pods. From there the process works pretty much the same, where the HPA polls these metrics and makes them available to the kubectl command.

The scaling policies are yaml files, and these will be applied to the default namespace if one is not explicitly specified with the "-n" option.


So in summary, I ran through these examples and was able to run the kubectl get --raw commands to pull metrics.

What I have not done yet, is to run the actual and load scale tests. I will update this blog post once I have done that.