A Dynamic Queue Adjustment Algorithm for Task Offloading in Vehicular Edge Computing Based on MADDPG

Abstract

Vehicular Edge Computing (VEC) has brought great convenience to people’s daily life. However, some problems still exist in VEC networks. Existing studies usually consider latency as an optimization objective, however, for some tasks, it is only necessary to ensure that they are completed within the deadline. For this reason, the urgency of the tasks is defined and, unlike the usual first-come-first-served waiting, the tasks are queued according to their urgency. Then, the optimization objective of the system is formulated and the offloading decision process is transformed into a Markov Decision Process(MDP),and our problem is solved by a deep reinforcement learning based algorithm. Simulation results show that the performance of our proposed algorithm is better than other algorithms.