Abstract: Aiming at the problem of high failure rate of task offloading caused by frequent switching between different edge servers of highly mobile vehicles in the scenario of Internet of vehicles, a novel predictive offloading decision algorithm was proposed. Firstly, the local server,edge server and cloud server computing models were constructed, and the offloading mode of tasks was predetermined based on the constraints of the size of computing tasks, maximum latency tolerance and server resources. Secondly, aiming at the offloading mode of edge servers, a vehicle location prediction model was constructed by using long short-term memory network to generate edge servers that could be used for offloading. Finally, the improved ant colony algorithm is used to realize the optimal offloading task allocation among multi-edge servers. Simulation results show that the proposed algorithm improves task completion rate and resource utilization rate.

Key words: vehicular networks, edge computing offloading, location prediction, long short term memory, ant colony optimization