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）是一种有望增强服务质量，特别是低延迟服务的技术，它使得可以将计算卸载到在附近的边缘服务器（ES）中。为避免网络拥塞，协作式边缘计算已成为一种新兴模式，可以使不同的ES协作共享他们的数据和计算资源。然而，大多数协作式边缘计算的论文只允许一次卸载，这可能由于ES一跳外的较差信道条件或计算工作量而限制了计算资源共享。通过允许ES多跳方便地共享计算工作负荷，多跳MEC使得更多的ES可以共享他们的计算资源。受此启发，我们提出了利用城市中无处不在的车辆形成数据传输网络的多跳任务卸载框架。为此，我们提出了一种通用的车联网辅助MEC中的多跳任务卸载框架，用户的任务可以通过潜在的多跳传输卸载到强大的ES。在所提出的框架下，我们开发了一种基于强化学习的任务离线方法，以解决由于车辆移动性和信道可变性而导致的维度诅咒问题，旨在在端到端延迟，频谱和计算资源的约束条件下最大化聚合服务吞吐量。数值结果表明，所提出的算法具有低复杂性并优于现有的基准方案。