Vehicles in platoons need to process many tasks to support various real-time vehicular applications. When a task arrives at a vehicle, the vehicle may not process the task due to its limited computation resource. In this case, it usually requests to offload the task to other vehicles in the platoon for processing. However, when the computation resources of all the vehicles in the platoon are insufficient, the task cannot be processed in time through offloading to the other vehicles in the platoon. Vehicular fog computing (VFC)-assisted platoon can solve this problem through offloading the task to the VFC which is formed by the vehicles driving near the platoon. Offloading delay is an important performance metric, which is impacted by both the offloading strategy for deciding where the task is offloaded and the number of the allocated vehicles in VFC to process the task. Thus, it is critical to propose an offloading strategy to minimize the offloading delay. In the VFC-assisted platoon system, vehicles usually adopt the IEEE 802.11p distributed coordination function (DCF) mechanism while having various computation resources. Moreover, when vehicles arrive and depart the VFC randomly, their tasks also arrive at and depart the system randomly. In this paper, we propose a semi-Markov decision process (SMDP) based offloading strategy while considering these factors to obtain the maximal long-term reward reflecting the offloading delay. Our research provides a robust strategy for task offloading in VFC systems, its effectiveness is demonstrated through simulation experiments and comparison with benchmark strategies.

翻译：车队中的车辆需要处理大量任务以支持多种实时车载应用。当任务到达某辆车辆时，由于其计算资源有限，该车辆可能无法处理该任务。此时，通常会请求将任务卸载至队列中的其他车辆处理。然而，当队列中所有车辆的计算资源均不足时，任务无法通过卸载至队列内其他车辆得到及时处理。基于车载雾计算（VFC）的车队系统可通过将任务卸载至由接近车队的车辆构成的VFC来解决此问题。卸载时延是重要的性能指标，其受决定任务卸载位置的卸载策略以及VFC中分配处理任务的车辆数量共同影响。因此，提出一种最小化卸载时延的卸载策略至关重要。在VFC辅助车队系统中，车辆通常采用IEEE 802.11p分布式协调功能（DCF）机制，并具备不同的计算资源。此外，当车辆随机加入或离开VFC时，其任务也随机到达或离开系统。本文综合考虑这些因素，提出一种基于半马尔可夫决策过程（SMDP）的卸载策略，以获取反映卸载时延的最大长期收益。本研究为VFC系统中的任务卸载提供了稳健策略，并通过仿真实验及与基准策略的对比验证了其有效性。