Maritime mobile edge computing offloading method based on deep reinforcement learning - Xin SU; Leilei MENG; Yiqing ZHOU; Wu CELIMUGE (Tongxin xuebao)
The strong heterogeneity among the network nodes of the maritime information system brings complex and high-dimensional constraints for optimizing task offloading of the maritime mobile edge computing.The complex and diverse maritime applications also lead to the overload processing of computing tasks in local areas of the maritime network.In order to optimize the task offloading and resource management of maritime network, as well as meet the maritime application service requirements of low-latency and high-reliability, a hierarchical classification method of maritime nodes based on multi-layers attributes and a novel offloading method for maritime mobile edge computing based on deep reinforcement learning were proposed.Compared with conventional methods, simulation results show that the proposed method can effectively reduce the computing task offloading delay of the marine information system, and maintain the robustness of the maritime network with large-scale task flows.