Joint Task Offloading and Scheduling Algorithm in Vehicular Edge Computing Networks

The rapid development of in-vehicle intelligent applications brings difficulties to traditional cloud computing in vehicular networks. Due to the long transmission distance between vehicles and cloud centers and the instability of communication links easily lead to high latency and low reliability. Vehicle edge computing (VEC), as a new computing paradigm, can improve vehicle quality of service by offloading tasks to edge servers with abundant computational resources. This paper studied a task offloading algorithm that efficiently optimize the delay cost and operating cost in a multi-user, multi-server VEC scenario. The algorithm solves the problem of execution location of computational tasks and execution order on the servers. In this paper, we simulate a real scenario where vehicles generate tasks through time lapse and the set of tasks is unknown in advance. The task set is preprocessed using a greedy algorithm and the offloading decision is further optimized using an optimization algorithm based on simulated annealing algorithm and heuristic rules. The simulation results show that compared with the traditional baseline algorithm, our algorithm effectively improves the task offloading utility of the VEC system.