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An Experimental Evaluation of the Kubernetes Cluster Autoscaler in the Cloud

Mulugeta Tamiru 1, 2 Johan Tordsson 1 Erik Elmroth 1 Guillaume Pierre 2
2 MYRIADS - Design and Implementation of Autonomous Distributed Systems
Inria Rennes – Bretagne Atlantique , IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
Abstract : Despite the abundant research in cloud autoscaling, autoscaling in Kubernetes, arguably the most popular cloud platform today, is largely unexplored. Kubernetes' Cluster Au-toscaler 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.
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https://hal.inria.fr/hal-02958916
Contributor : Guillaume Pierre <>
Submitted on : Tuesday, October 6, 2020 - 1:04:26 PM
Last modification on : Wednesday, October 14, 2020 - 3:54:51 AM

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Mulugeta Tamiru, Johan Tordsson, Erik Elmroth, Guillaume Pierre. An Experimental Evaluation of the Kubernetes Cluster Autoscaler in the Cloud. CloudCom 2020 - 12th IEEE International Conference on Cloud Computing Technology and Science, Dec 2020, Bangkok, Thailand. ⟨hal-02958916⟩

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