MSM: Mobility-Aware Service Migration for Seamless Provision: A Data-Driven Approach

Abstract

Mobile Edge Computing (MEC) is a promising approach to support high-quality time-sensitive applications. With the increasing number of mobile devices, achieving efficient service migration management has become non-trivial in MEC. In addition, the service migration issue is difficult to be solved in real-time due to user mobility and dynamic network conditions. In this paper, we investigate the mobility-aware service migration problem in MEC by introducing a data-driven framework. Firstly, service migration is formulated as an optimization problem for minimizing the long-term system delay that consists of computing, communication, and migration delays. Secondly, we propose a Mobility-aware Service Migration scheme, named MSM, consisting of three layers: 1) the data collection layer; 2) the association patterns analysis layer; and 3) the service migration layer. Specifically, we first collect users’ historical Wi-Fi traces to mine the association patterns. We then design a user management mechanism to reduce the complexity of decision-making by using user association patterns. Finally, we formulate the service migration as a 2D-Markov decision process and devise a deep reinforcement learning (DRL) based algorithm to obtain service migration decisions in a large-scale MEC scenario. Extensive data-driven experiments are conducted to demonstrate the efficacy of MSM in reducing the system delay.