Tuning the Kubernetes HPA in GKE

The Kubernetes Horizontal Pod Autoscaler (HPA) is a fundamental tool for managing the scalability and efficiency of your environment, working by deploying more Pods in response to increased load. However, achieving the best price-performance with HPA requires a nuanced understanding of its settings, particularly your CPU utilization targets. The common assumption that a 50% CPU target is a good starting point can actually lead to higher costs. In fact, the 50% HPA CPU target might require significantly more provisioned resources compared to a 70% target, with a marginal impact on performance. And sometimes, changing settings such as resource requests on Pods can actually deliver a better balance between cost, performance and agility.

In this blog, we explore why that is, so you can learn more about fundamental HPA optimization strategies for Google Kubernetes Engine (GKE).

The resource efficiency conundrum

On the surface, setting HPA at a 50% CPU utilization target ...