With the rapid advancement of Intelligent Transportation Systems (ITS) and vehicular communications, Vehicular Edge Computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment.

翻译：随着智能交通系统（ITS）及车载通信技术的快速发展，车载边缘计算（VEC）正成为支撑低延迟ITS应用与服务的重要技术。本文针对异构车载边缘计算场景中移动车辆/用户的计算卸载问题展开研究，聚焦于网络与基站选择问题，其中不同网络具有不同的流量负载。在快速变化的车辆环境中，由于与基站共址的边缘计算服务器拥塞所导致的延迟，会显著影响用户的计算卸载体验。然而，此类环境的非平稳特性及信息稀缺性使得拥塞预测成为一项复杂任务。为应对这一挑战，我们基于多臂赌博机理论提出了一种在线学习算法与一种离策略学习算法。这些算法通过利用历史卸载数据预测所卸载任务的延迟，在分段平稳环境中动态选择负载最低的网络。此外，为最小化车辆移动性导致的任务丢失，我们开发了一种基站选择方法。同时，针对所选网络提出了一种基于车辆停留时间的中继机制。通过大量数值分析，我们证明所提出的基于学习的解决方案能够通过选择负载最低的网络适应网络流量变化，从而降低卸载任务的延迟。此外，我们证明所提出的联合基站选择与中继机制能够在车辆环境中最小化任务丢失。