Container Scheduling with Dynamic Computing Resource for Microservice Deployment in Edge Computing

With the massive increase of Internet of Things devices and their data, executing applications by using micro-service architecture has emerged as the predominant trend. As the container technology emerges, microservices can be lightweightly deployed in resource-constrained edge nodes. However, existing container scheduling algorithms often overlook the allocation of computing resources on edge servers. When multiple containers are assigned to an edge node, it is usually assumed that they share a CPU frequency, which is obviously unrealistic. In this paper, we first formulate an online container-based microservice scheduling problem with dynamic computing power to minimize the total delay and energy consumption, where we need to determine the assignment between microservices and edge nodes and the allocation of computing power to each microservice in an edge node. Then, we propose a Soft Actor-Critic (SAC) based reinforcement learning algorithm to address this problem, where a GRU unit is designed in the policy network to extract the correlation among multiple decisions, and an action selection mechanism is given to speed up the convergence. Finally, a simulated scheduling system is implemented to validate our algorithm, which demonstrates that our algorithm outperforms the commonly used baselines by up to 65% in terms of the total objective on average.