Energy-Delay-Aware Joint Microservice Deployment and Request Routing With DVFS in Edge: A Reinforcement Learning Approach

The emerging microservice architecture offers opportunities for accommodating delay-sensitive applications in edge. However, such applications are computation-intensive and energy-consuming, imposing great difficulties to edge servers with limited computing resources, energy supply, and cooling capabilities. To reduce delay and energy consumption in edge, efficient microservice orchestration is necessary, but significantly challenging. Due to frequent communications among multiple microservices, service deployment and request routing are tightly-coupled, which motivates a complex joint optimization problem. When considering multi-instance modeling and fine-grained orchestration for massive microservices, the difficulty is extremely enlarged. Nevertheless, previous work failed to address the above difficulties. Also, they neglected to balance delay and energy, especially lacking dynamic energy-saving abilities. Therefore, this paper minimizes energy and delay by jointly optimizing microservice deployment and request routing via multi-instance modeling, fine-grained orchestration, and dynamic adaptation. Our queuing network model enables accurate end-to-end time analysis covering queuing, computing, and communicating delays. We then propose a delay-aware reinforcement learning algorithm, which derives the static service deployment and routing decisions. Moreover, we design an energy-aware dynamic frequency scaling algorithm, which saves energy with fluctuating request patterns. Experiment results demonstrate that our approaches significantly outperform baseline algorithms in both delay and energy consumption.