An Experimental Evaluation of the Kubernetes Cluster Autoscaler in the Cloud

Abstract

Despite the abundant research in cloud autoscaling, autoscaling in Kubernetes, arguably the most popular cloud platform today, is largely unexplored. Kubernetes' Cluster Autoscaler can be configured to select nodes either from a single node pool (CA) or from multiple node pools (CA-NAP). We evaluate and compare these configurations using two representative applications and workloads on Google Kubernetes Engine (GKE). We report our results using monetary cost and standard autoscaling performance metrics (under- and over-provisioning accuracy, under- and over-provisioning timeshare, instability of elasticity and deviation from the theoretical optimal autoscaler) endorsed by the SPEC Cloud Group. We show that, overall, CA-NAP outperforms CA and that autoscaling performance depends mainly on the composition of the workload. We compare our results with those of the related work and point out further configuration tuning opportunities to improve performance and cost-saving.