Offloading Strategy for Vehicles in the Architecture of Vehicle-MEC-Cloud

With the rapid development of the Internet of Vehicles, vehicles need to complete lots of computing tasks in time. However, there have been more strict demands for latency due to limited resources on board. In this paper, we propose an offloading strategy for vehicular tasks to minimize the maximum delay in the Vehicle-MEC-Cloud architecture. Firstly, considering the urgency level of tasks, a novel price competition model is constructed based on the queuing model and preemptive scheduling algorithm. Then, in the Vehicle-MEC-Cloud architecture, we fully utilize the limited resources of each vehicle itself and formulate the optimization problem. Finally, we utilize a variational inequality to convert the non-convex problem as a convex one, and propose a optimization algorithm based on the combination of gradient projection shrinkage algorithm and greedy algorithm to solve the problem. The simulation results demonstrate that the proposed strategy outperforms other existing strategies significantly in terms of delay and offloading ratio.