Multi-user reinforcement learning based task migration in mobile edge computing

Mobile Edge Computing (MEC) is a promising approach. Dynamic service migration is a key technology in MEC. In order to maintain the continuity of services in a dynamic environment, mobile users need to migrate tasks between multiple servers in real time. Due to the uncertainty of movement, frequent migration will increase delays and costs and non-migration will lead to service interruption. Therefore, it is very challenging to design an optimal migration strategy. In this paper, we investigate the multi-user task migration problem in a dynamic environment and minimizes the average service delay while meeting the migration cost. In order to optimize the service delay and migration cost, we propose an adaptive weight deep deterministic policy gradient (AWDDPG) algorithm. And distributed execution and centralized training are adopted to solve the high-dimensional problem. Experiments show that the proposed algorithm can greatly reduce the migration cost and service delay compared with the other related algorithms.