Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Method

Abstract--In this paper, we investigate a multi-user offloading problem in the overlapping domain of a multi-server mobile edge computing system. We divide the original problem into two stages: the offloading decision making stage and the request scheduling stage. To prevent the terminal from going out of service area during offloading, we consider the mobility parameter of the terminal according to the human behaviour model when making the offloading decision, and then introduce a server evaluation mechanism based on both the mobility parameter and the server load to select the optimal offloading server. In order to fully utilise the server resources, we design a double deep Q-network (DDQN)-based reward evaluation algorithm that considers the priority of tasks when scheduling offload requests. The authors of [3] proposed an effective task scheduling algorithm based on dynamic priority, which significantly reduced With the development of Multi-access Edge Computing task completion time and improved QoS. In [4], the authors (MEC) technology, MEC servers are moving closer to the proposed a hybrid task offloading scheme based on deep reinforcement terminal devices (TDs), which can be served more efficiently learning that achieved vehicle-to-edge and vehicleto-vehicle as the transmission latency is greatly reduced [1].