Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Method
<|reference_start|>Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Method: 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.<|reference_end|>
@article{wang2024computation,
    title={Computation Offloading for Multi-server Multi-access Edge Vehicular
  Networks: A DDQN-based Method},
    author={Siyu Wang, Bo Yang, Zhiwen Yu, Xuelin Cao, Yan Zhang, Chau Yuen},
    journal={arXiv preprint arXiv:2404.07215},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07215},
    primaryClass={cs.NI cs.AI eess.SP}
}