Hybrid autoscaling strategy on container-based cloud platform

Abstract

The container has several advantages over the traditional virtual machine technology such as light-weight, fast booting time, and fast recovery. Kubernetes is one the most outstanding container management and deployment platforms. The Kubernetes provides autoscaling function, which will increase and decrease the hardware resources to adapt with the current traffic load situation to keep the user experience. Two popular autoscaling methods are horizontal autoscaling and vertical autoscaling. Based on the monitoring resource utilization, horizontal autoscaling will increase the number of PoDs (point of deployment) or vertical autoscaling will increase the hardware resources of each PoD to achieve the target utilization. In this paper, we present a hybrid solution that combines the advantages of both autoscaling solutions and proposes a bandwidth-efficient scheduler strategy. By numerical analysis, our hybrid approach is better than the normal HPA approach in terms of bandwidth cost and has lower autoscaling latency than the VPA approach