Energy-Efficient Joint Deployment and Routing for Delay-Sensitive Microservices in Edge Computing

Microservice as a promising architecture has been widely employed in edge computing to support sensitive-latency online applications. Unfortunately, the deployment of numerous microservices creates complex invocations and requires frequent communications, which brings significant challenges to service deployment and request routing. Moreover, the strict requirements for low energy consumption and low latency in edge computing further exacerbate these difficulties. In this case, it is crucial to optimize the joint microservices deployment and request routing using a meticulous and energy-efficient approach. However, existing studies often overlook their interdependence and treat them as separate problems. Therefore, we propose a fine-grained approach in this paper to jointly optimize the deployment and request routing of microservices within edge computing scenarios. First, we utilize queuing networks to conduct detailed modeling and mathematical analysis that study the complex invocation relationships, microservice instance sharing, and communication latency. Second, we propose an energy-efficient microservice orchestration algorithm, referred to as Cluster-Processing-based Adaptive Memory Procedure. This algorithm maintains a memory storing elite solution elements, and it iteratively picks up suitable elements from the memory to construct superior solutions. Finally, extensive simulation experiments demonstrate that the proposed algorithm outperforms baseline algorithms significantly in terms of response latency and energy consumption.