Adaptive Service Function Chain Scheduling in Mobile Edge Computing via Deep Reinforcement Learning

Abstract

MEC (Mobile Edge Computing) provides both IT service environment and cloud computation on the edge of the network. This technology not only minimizes the end-to-end latency but also increases the efficiency of computing. Some latency-sensitive applications, such as cloud video, online game, and augmented reality, take advantage of the MEC system to provide fast and stable services. Several new network techniques, including the implementation of NFV (Network Function Virtualization), the placement of VNF (Virtual Network Function) and the scheduling of SFC (Service Function Chain), should be considered to be applied in the MEC system. In this paper, we focus on the research about the scheduling of SFC in the NFV enabled MEC system and propose a solution accordingly. First, we make reasonable assumptions on the settings of MEC systems and model the SFC scheduling problem into a flexible job-shop scheduling problem. Since minimizing the latency can significantly improve the quality of service (QoS) and increase the revenue of Internet Service Providers, our optimization goal is to minimize the overall scheduling latency. To solve this optimization problem, a deep reinforcement learning based algorithm DQS is proposed. DQS can detect the variation of the MEC system's environment and perform adaptive scheduling for SFC requests. As the results of the simulation indicate, DQS works better than the other off-the-shelf algorithms in two key indexes: overall scheduling latency and average resource usage. Moreover, DQS can shorten the decision time and schedule SFCs stably with high performance. It is suitable to be extended to an online scheduling algorithm.