Proactive Autoscaling for Edge Computing Systems with Kubernetes

TL;DR

This paper introduces a proactive pod autoscaler for Kubernetes that forecasts workloads using custom metrics, improving resource efficiency and application performance in edge computing environments.

Contribution

It presents a novel proactive autoscaling method that predicts workload changes for edge applications, outperforming default Kubernetes autoscaling.

Findings

Outperforms default Kubernetes autoscaler in resource utilization

Improves application performance in CPU-intensive edge tasks

Forecasts workloads using multiple custom metrics

Abstract

With the emergence of the Internet of Things and 5G technologies, the edge computing paradigm is playing increasingly important roles with better availability, latency-control and performance. However, existing autoscaling tools for edge computing applications do not utilize heterogeneous resources of edge systems efficiently, leaving scope for performance improvement. In this work, we propose a Proactive Pod Autoscaler (PPA) for edge computing applications on Kubernetes. The proposed PPA is able to forecast workloads in advance with multiple user-defined/customized metrics and to scale edge computing applications up and down correspondingly. The PPA is optimized and evaluated on an example CPU-intensive edge computing application further. It can be concluded that the proposed PPA outperforms the default pod autoscaler of Kubernetes on both efficiency of resource utilization and application performance. The article also highlights future possible improvements on the proposed PPA.