Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the VFC system according to the 802.11p standard and processed by the computation resources in the VFC system. The delay of task offloading, consisting of the transmission delay and computing delay, is extremely critical especially for some delay-sensitive applications. Furthermore, the long-term reward of the system (i.e., jointly considers the transmission delay, computing delay, available resources, and diversity of vehicles and tasks) becomes a significantly important issue for providers. Thus, in this article, we propose an optimal task offloading scheme to maximize the long-term reward of the system where 802.11p is employed as the transmission protocol for the communications between vehicles. Specifically, a task offloading problem based on a semi-Markov decision process (SMDP) is formulated. To solve this problem, we utilize an iterative algorithm based on the Bellman equation to approach the desired solution. The performance of the proposed scheme has been demonstrated by extensive numerical results.
翻译:在自动车辆网络中,任务卸载是高效处理计算密集型任务的关键技术。在任务卸载中,任务按照802.11p标准传递给VFC系统,并由VFC系统计算资源处理。任务卸载的延误,包括传输延迟和计算延迟,对于某些延迟敏感应用来说,是极为关键的。此外,系统的长期奖励(即,共同考虑传输延迟、计算延迟、可用资源以及车辆和任务的多样性)对于供应商来说是一个非常重要的问题。因此,在本条中,我们提议了一项最佳任务卸载计划,以最大限度地提高系统的长期奖励,即802.11p作为车辆之间通信的传输协议。具体地说,基于半马尔科夫决定程序(SMDP)的任务卸载问题非常关键。为了解决这一问题,我们采用了基于贝尔曼方程式的迭代算算法,以理想的数字解决方案为广泛的数字方案展示了业绩。