Abstract: Aiming at the problems of high computing cost of tasks and unbalanced load of edge nodes in vehicular edge computing (VEC), combined software defined network (SDN) with multi-edge computing, a three-layer software defined vehicular edge computing model of "end-multi-edge-cloud" (SDVEC) was constructed, and a multi-edge nodes cooperative offloading and resource allocation algorithm (MCORA-KDQN) was proposed. The SDN controller obtained network information from the global perspective, and uniformly scheduled task offloading and resource allocation. In the algorithm, the improved K-Means algorithm was adopted to divide the task into local cluster, edge nodes cluster and cloud server cluster respectively, in order to determine the initial offloading decision of the task, and then the deep q network (DQN) algorithm was used to obtain the optimal offloading decision, offloading proportion and resource allocation strategy of the task in the edge nodes cluster. The simulation results show that compared with the comparison algorithm, the proposed algorithm reduces the task computing cost by at least 18.6%, improves the resource utilization rate of edge nodes by at least 22.9%, and realizes the load balance among edge nodes.

Key words: vehicular edge computing, software defined network, cooperative offloading, resource allocation, K-Means, deep q network