Kubernetes Capacity and Resource Management: It's Not What You Think It Is

• autoscaler

  89 7,602 9.5 Go

  Autoscaling components for Kubernetes

  

One way Kubernetes allows to automate this process is via VerticalPodAutoscalers (VPA). VPAs, in its default mode, provide you with recommendations on how to set your resources. Those recommendations are based on historical usage, as found in the metrics server. You should configure VPA to use Prometheus as the metrics storage — otherwise VPA relies only on real-time metrics.

• keda

  91 7,705 9.5 Go

  KEDA is a Kubernetes-based Event Driven Autoscaling component. It provides event driven scale for any container running in Kubernetes

  

I am oversimplifying here a lot, of course. The focus of this article is primarily on CPU and RAM, but you can configure HPA to scale your pods based on any custom metrics. There are also projects like KEDA that introduce even-driven auto-scaling for the pods.

• InfluxDB

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  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

• karpenter-provider-aws

  46 5,823 9.9 Go

  Karpenter is a Kubernetes Node Autoscaler built for flexibility, performance, and simplicity.

  

There are tools that you can deploy on your cluster to simplify this process a bit. One of them, Karpenter, aims to automate provisioning the right nodes depending on your pod needs. This way, you just trust Karpeneter to create a node that can fit a pod requesting 10Gb of RAM if such node appears in the cluster. It’s similar to how Google Cloud GKE Autopilot is working, to which we will come back at the end of the article.

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