Mobility-aware partial computation offloading in vehicular networks: A deep reinforcement learning based scheme

Abstract

Encouraged by next-generation networks and autonomous vehicle systems, vehicular networks must employ advanced technologies to guarantee personal safety, reduce traffic accidents and ease traffic jams. By leveraging the computing ability at the network edge, multi-access edge computing (MEC) is a promising technique to tackle such challenges. Compared to traditional full offloading, partial offloading offers more flexibility in the perspective of application as well as deployment of such systems. Hence, in this paper, we investigate the application of partial computing offloading in-vehicle networks. In particular, by analyzing the structure of many emerging applications, e.g., AR and online games, we convert the application structure into a sequential multi-component model. Focusing on shortening the application execution delay, we extend the optimization problem from the single-vehicle computing offloading (SVCOP) scenario to the multi-vehicle computing offloading (MVCOP) by taking multiple constraints into account. A deep reinforcement learning (DRL) based algorithm is proposed as a solution to this problem. Various performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay.