ABSTRACT
The advantage of mobile edge computing (MEC) is that the computing and storage resources can be distributed in all parts of the network, and the deployment node of MEC also meets the requirements of an application for low latency. However, user mobility may make it far from the edge server which undertakes the application task, resulting in inevitable service interruption. In this paper, a new virtual machine (VM) service migration scheme supporting mobility is proposed. Our scheme is realized in three aspects: (1) Some VMs in the related edge server can host the multiuser application tasks. The VM migration strategy can properly migrate the user’s tasks, reduce the user-perceived delay, and ameliorate the quality of service (QoS); (2) The system dynamically allocates resources to users, including bandwidth resources and computing resources, which affects the perceived delay of users; (3) We further propose a multiuser service migration scheme based on deep reinforcement learning (DRL), which can reduce the large state space and realize fast decision-making. We conduct extensive experiments, which show that using the DRL algorithm outperforms the classical RL algorithm and some other baseline algorithm.
