Unleashing the Scalability Potential of Power-Constrained Data Center in the Microservice Era

Recent scale-out cloud services have undergone a shift from monolithic applications to microservices by putting each functionality into lightweight software containers. Although traditional data center power optimization frameworks excel at per-server or per-rack management, they can hardly make informed decisions when facing microservices that have different QoS requirements on a per-service basis. In a power-constrained data center, blindly budgeting power usage could lead to a power unbalance issue: microservices on the critical path may not receive adequate power budget. This unavoidably hinders the growth of cloud productivity. To unleash the performance potential of cloud in the microservice era, this paper investigates microservice-aware data center resource management. We model microservice using a bipartite graph and propose a metric called microservice criticality factor (MCF) to measure the overall impact of performance scaling on a microservice from the whole application's perspective. We further devise ServiceFridge, a novel system framework that leverages MCF to jointly orchestrate software containers and control hardware power demand. Our detailed case study on a practical microservice application demonstrates that ServiceFridge allows data center to reduce its dynamic power by 25% with slight performance loss. It improves the mean response time by 25.2% and improves the 90th tail latency by 18.0% compared with existing schemes.