Proactive Resource Autoscaling Scheme Based on SCINet for High-Performance Cloud Computing

The container resource autoscaling technique provides scalability to cloud services composed of microservice architecture in a cloud-native computing environment. However, the service efficiency is reduced as the scaling is delayed because dynamic loads occur with various workload patterns. Furthermore, estimating the efficient resource size for the workload is difficult, resulting in resource waste and overload. Therefore, this study proposes high-performance resource management (HiPerRM), which stably and elastically manages container resources to ensure service scalability and efficiency even under rapidly changing dynamic loads. HiPerRM forecasts future workloads using a sample convolutional and interaction network (SCINet) model applied with the reversible instance normalization (RevIN) method. HiPerRM generates a resource request with an elastic size based on the forecasted CPU and memory usage, and then efficiently adjusts the pod's resource request and the number of replicas via HiPerRM's VPA (Hi-VPA) and HiPerRM's HPA (Hi-HPA). As a result of evaluating the performance of HiPerRM, the average resource utilization was improved by approximately 3.96–34.06% compared to conventional autoscaling techniques, even when the resource size was incorrectly estimated for various workloads, and there were relatively fewer overloads.

I. Introduction

The cloud-native computing paradigm has emerged rapidly due to the advantages of microservice architecture, which provides improved scalability and stability for cloud applications compared to monolithic architecture [1]. Containers that are lighter than virtual machines are commonly utilized to handle microservices efficiently [2], [3]. As these containers are vast and complicated, they are managed via a container orchestration platform that provides a container automation management technique [4], [5]. Kubernetes, among various platforms, provides a reactive mechanism-based resource autoscaling technique to provide scalability and availability of microservices from complicated workloads, and is primarily comprised of vertical pod autoscaling (VPA) and horizontal pod autoscaling (HPA) [6], [7].