Multi-access edge computing (MEC) is a promising technology to enhance the quality of service, particularly for low-latency services, by enabling computing offloading to edge servers (ESs) in close proximity. To avoid network congestion, collaborative edge computing has become an emerging paradigm to enable different ESs to collaboratively share their data and computation resources. However, most papers in collaborative edge computing only allow one-hop offloading, which may limit computing resource sharing due to either poor channel conditions or computing workload at ESs one-hop away. By allowing ESs multi-hop away to also share the computing workload, a multi-hop MEC enables more ESs to share their computing resources. Inspired by this observation, in this paper, we propose to leverage omnipresent vehicles in a city to form a data transportation network for task delivery in a multi-hop fashion. Here, we propose a general multi-hop task offloading framework for vehicle-assisted MEC where tasks from users can be offloaded to powerful ESs via potentially multi-hop transmissions. Under the proposed framework, we develop a reinforcement learning based task offloading approach to address the curse of dimensionality problem due to vehicular mobility and channel variability, with the goal to maximize the aggregated service throughput under constraints on end-to-end latency, spectrum, and computing resources. Numerical results demonstrate that the proposed algorithm achieves excellent performance with low complexity and outperforms existing benchmark schemes.

翻译：多接入边缘计算（MEC）是一种有前景的技术，通过将计算任务卸载至邻近的边缘服务器（ES），能够提升服务质量，尤其适用于低时延业务。为避免网络拥塞，协作边缘计算已成为一种新兴范式，使不同ES能够协同共享其数据与计算资源。然而，现有协作边缘计算研究大多仅允许单跳卸载，由于信道条件恶劣或一跳范围内ES的计算负载限制，这种机制可能制约计算资源共享。通过允许多跳范围的ES分担计算负载，多跳MEC可使更多ES共享计算资源。受此启发，本文提出利用城市中无处不在的车辆构建数据运输网络，以多跳方式实现任务交付。为此，我们提出一种面向车辆辅助MEC的通用多跳任务卸载框架，用户任务可通过潜在的多跳传输卸载至高性能ES。在该框架下，我们开发了基于强化学习的任务卸载方法，以应对车辆移动性与信道时变性带来的维度灾难问题，目标是在端到端时延、频谱和计算资源约束下最大化聚合服务吞吐量。数值结果表明，所提算法在低复杂度下实现了优异性能，并优于现有基准方案。