Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Method
This addresses efficient resource allocation for vehicular networks, but it is incremental as it builds on existing deep reinforcement learning techniques.
The paper tackles the problem of computation offloading in multi-server vehicular edge networks by dividing it into decision-making and scheduling stages, incorporating mobility and server load, and using a DDQN-based algorithm, resulting in performance improvements over traditional methods and DQN as verified by simulations.
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. Finally, numerical simulations are conducted to verify that our proposed method outperforms traditional mathematical computation methods as well as the DQN algorithm.