A Proactive Stable Scheme for Vehicular Collaborative Edge Computing

Abstract

Due to the restricted computing resources and high upgrading costs, onboard processors alone cannot meet the quality of service (QoS) requirements of the emerging and constantly upgrading vehicular applications. Computation offloading is a feasible solution to the excessive computation-intensive tasks. Meanwhile, vehicular collaborative edge computing (VCEC) is a paradigm to make the best use of surrounding vehicles' idle computing resources when the task density on a specific road segment suddenly increases. However, the high mobility of vehicles and the ad hoc nature of vehicular networks make maintaining stable task offloading performance quite challenging. Especially when the idle computing resources surrounding vehicles are insufficient, there has not been researching to achieve stable offloading performance. Based on this, we consider a proactive strategy that can decrease the events that affect performance stability. First, we propose a mobility prediction model for future network status prediction. Then we design an adaptive task offloading scheme based on proactive adjusting (PATO) to maintain stable task offloading performance. The scheme includes a state processing model and a deep reinforcement learning (DRL)-based task offloading algorithm. Finally, we conduct extensive simulations in various scenarios with insufficient resources to validate the task offloading performance of PATO. Compared with the existing DRL-based algorithm, the simulation results show that PATO can improve the mean offloading utility by 95.4% and the completion ratio by 15.8%.