Saver: a proactive microservice resource scheduling strategy based on STGCN

Abstract

As container technology and microservices mature, applications increasingly shift to microservices and cloud deployment. Growing microservices scale complicates resource scheduling. Traditional methods, based on fixed thresholds, are simple but lead to resource waste and poor adaptability to traffic spikes. To address this problem, we design a new resource scheduling strategy Saver based on the container cloud platform, which combines a microservice request prediction model with a microservice performance evaluation model that predicts SLO (Service Level Objective) violations and a heuristic algorithm to solve the optimal resource scheduling for the cluster. We deploy the microservices open-source project sock-shop in a Kubernetes cluster to evaluate Saver. Experimental results show that Saver saves 7.9% of CPU resources, 13% of the instances, and reduces the SLO violation rate by 31.2% compared to K8s autoscaler.