Kubernetes v1.33: HorizontalPodAutoscaler Configurable Tolerance

Kubernetes v1.33: HorizontalPodAutoscaler Configurable Tolerance What is it? How do I use it? I want all the details! This post describes configurable tolerance for horizontal Pod autoscaling , a new alpha feature first available in Kubernetes 1.33. Horizontal Pod Autoscaling is a well-known Kubernetes feature that allows your workload to automatically resize by adding or removing replicas based on resource utilization. Let's say you have a web application running in a Kubernetes cluster with 50 replicas. You configure the HorizontalPodAutoscaler (HPA) to scale based on CPU utilization, with a target of 75% utilization. Now, imagine that the current CPU utilization across all replicas is 90%, which is higher than the desired 75%. The HPA will calculate the required number of replicas using the formula: In this example: So, the HPA will increase the number of replicas from 50 to 60 to reduce the load on each pod. Similarly, if the CPU utilization were to drop below 75%, the HPA would scale down the number of replicas accordingly. The Kubernetes documentation provides a detailed description of the scaling algorithm. In order to avoid replicas being created or deleted whenever a small metric fluctuation occurs, Kubernetes applies a form of hysteresis: it only changes the number of replicas when the current and desired metric values differ by more than 10%. In the example above, since the ratio between the current and desired metric values is \(90/75\), or 20% above target, exceeding the 10% tolerance, the scale-up action will proceed. This default tolerance of 10% is cluster-wide; in older Kubernetes releases, it could not be fine-tuned. It's a suitable value for most usage, but too coarse for large deployments, where a 10% tolerance represents tens of pods.