Kubernetes Cluster Autoscaler: How It Works and How to Tune Scale-Up and Scale-Down
Blog post from Cast AI
The Kubernetes Cluster Autoscaler (CA) is a tool designed to automatically adjust the number of nodes in a cluster based on the demand of pending pods. It is a reactive system that adds nodes when pods are pending due to insufficient resources and removes underutilized nodes after a default window of 10 minutes. CA operates within predefined node groups and relies on pod resource requests to measure utilization, which can lead to inefficiencies if requests are inflated. It is not predictive and cannot prefetch nodes, adding a latency factor to job start times. The document discusses tuning parameters that can help optimize costs, such as adjusting the expander to "least-waste" and modifying scale-down thresholds. However, structural limitations remain, such as its reliance on requests rather than actual usage, and its slower provisioning time compared to alternatives like Karpenter, which offers faster node provisioning and more dynamic instance selection. While CA is stable and integrates well with existing infrastructure, especially for users on Google Kubernetes Engine (GKE), Karpenter is recommended for those on AWS or Azure who need faster scaling capabilities. The text emphasizes that accurate resource requests are crucial for effective autoscaling with either tool, and highlights Cast AI's potential to address inflated request issues by adjusting them based on actual consumption.
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