Various emerging vehicular applications such as autonomous driving and safety early warning are used to improve the traffic safety and ensure passenger comfort. The completion of these applications necessitates significant computational resources to perform enormous latency-sensitive/nonlatency-sensitive and computation-intensive tasks. It is hard for vehicles to satisfy the computation requirements of these applications due to the limit computational capability of the on-board computer. To solve the problem, many works have proposed some efficient task offloading schemes in computing paradigms such as mobile fog computing (MFC) for the vehicular network. In the MFC, vehicles adopt the IEEE 802.11p protocol to transmit tasks. According to the IEEE 802.11p, tasks can be divided into high priority and low priority according to the delay requirements. However, no existing task offloading work takes into account the different priorities of tasks transmitted by different access categories (ACs) of IEEE 802.11p. In this paper, we propose an efficient task offloading strategy to maximize the long-term expected system reward in terms of reducing the executing time of tasks. Specifically, we jointly consider the impact of priorities of tasks transmitted by different ACs, mobility of vehicles, and the arrival/departure of computing tasks, and then transform the offloading problem into a semi-Markov decision process (SMDP) model. Afterwards, we adopt the relative value iterative algorithm to solve the SMDP model to find the optimal task offloading strategy. Finally, we evaluate the performance of the proposed scheme by extensive experiments. Numerical results indicate that the proposed offloading strategy performs well compared to the greedy algorithm.

中文翻译：

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车载网络中基于802.11p的基于云的移动雾计算系统的高效任务分载

诸如自动驾驶和安全预警之类的各种新兴车辆应用被用于改善交通安全并确保乘客舒适度。这些应用程序的完成需要大量的计算资源来执行庞大的对延迟敏感/对非延迟敏感的计算密集型任务。由于车载计算机的有限计算能力，车辆很难满足这些应用的计算要求。为了解决该问题，许多工作提出了一些有效的任务卸载方案，以用于计算范例，例如用于车辆网络的移动雾计算（MFC）。在MFC中，车辆采用IEEE 802.11p协议来传输任务。根据IEEE 802.11p，可以根据延迟要求将任务分为高优先级和低优先级。但是，现有的任务分流工作都没有考虑由IEEE 802.11p的不同访问类别（AC）传输的任务的不同优先级。在本文中，我们提出了一种有效的任务卸载策略，以从减少任务的执行时间方面最大化长期预期的系统回报。具体而言，我们共同考虑了由不同AC传输的任务的优先级，车辆的机动性以及计算任务的到达/离开的影响，然后将卸载问题转换为半马尔可夫决策过程（SMDP）模型。然后，我们采用相对值迭代算法对SMDP模型进行求解，以找到最优的任务卸载策略。最后，我们通过广泛的实验评估了该方案的性能。