For vehicles with limited computation resources offloading their computational tasks to a mobile edge computing (MEC) server has been studied in the past as an effective means for improving their computational capabilities. However, most of these studies do not consider, in a meaningful way, the economic aspects related to both the computation offloading of the vehicles and the MEC service providers. In order to fill this gap, in this paper, a new cost based optimization methodology which jointly considers the cost of partial offloading vs. the pricing of the MEC server is proposed and its performance is analyzed. In particular, we first formally establish the cost model for vehicles and then, by setting a service price, the revenue model for MEC server. Secondly, optimal vehicle offloading strategies are identified and through a cost minimization partial computation offloading algorithm vehicles can configure, in an optimal way, the local CPU frequency and task partition based on the service price. Thirdly, by considering its computation resource limitations, the resource allocation and pricing mechanism for the MEC server is presented. It is shown that, through the development of an appropriate pricing algorithm, the MEC server can obtain the service price which maximizes its revenue while at the same time satisfying the server’s resource constraints. Numerical results have verified that the proposed scheme is indeed more cost effective as compared to local execution with dynamic voltage scaling (DVS) technique, full computation offloading and other partial computation offloading schemes. Furthermore, various performance evaluation results obtained by means of computer simulations have shown that the proposed pricing scheme achieves higher revenue as compared to other previously known fixed and random pricing schemes.

对于计算资源有限的车辆，过去已经研究了将其计算任务卸载到移动边缘计算（MEC）服务器的方法，以此作为提高其计算能力的有效手段。但是，这些研究大多数都没有以有意义的方式考虑与车辆的计算卸载和MEC服务提供商有关的经济方面。为了填补这一空白，本文提出了一种新的基于成本的优化方法，该方法结合了部分卸载的成本与MEC服务器的定价，并对其性能进行了分析。特别是，我们首先正式建立车辆的成本模型，然后通过设置服务价格确定MEC服务器的收入模型。其次，确定最佳的车辆卸载策略，并且通过成本最小化，局部计算卸载算法，车辆可以基于服务价格以最佳方式配置本地CPU频率和任务划分。第三，考虑到计算资源的局限性，提出了MEC服务器的资源分配和定价机制。结果表明，通过适当定价算法的开发，MEC服务器可以获得服务价格，该价格可以最大化其收益，同时满足服务器的资源约束。数值结果证明，与采用动态电压缩放（DVS）技术，完全计算分载和其他部分计算分载方案的本地执行相比，该方案确实更具成本效益。此外，