Service caching can improve the QoS of computationally intensive vehicle applications by pre-storing the necessary application programs and related data for computing tasks on edge servers. In this paper, we propose a new vehicle edge computing framework based on software defined networks, which introduces the reputation to measure the contribution of each vehicle as the basis for providing different quality of services. The process is divided into two phases: in the first phase, the vehicle requests the offload application task from the edge server; and in the second phase, the edge server makes the service caching decision after processing the task. We design the whole interaction process as a kind of incentive mechanism based on reputation via using Stackelberg game modeling, and analyze the optimal strategy for both sides of the game by reverse induction. Furthermore, we also prove the existence and uniqueness of Stackelberg equilibrium in two-stage game, and a genetic optimization algorithm is designed to quickly obtain the optimal strategy for both sides of the game. Experimental results show that the proposed scheme not only brings more profits to the edge server side, but also reduces the average delay by 76 % compared with the ordinary mobile edge computing scheme.

通过预先存储边缘服务器上的计算任务所需的应用程序和相关数据，服务缓存可以提高计算密集型车辆应用程序的QoS。在本文中，我们提出了一种基于软件定义网络的新的车辆边缘计算框架，该框架引入了声誉来衡量每辆汽车的贡献，以此作为提供不同服务质量的基础。该过程分为两个阶段：第一阶段，车辆从边缘服务器请求卸载应用程序任务；第二阶段，车辆向边缘服务器请求卸载应用程序任务。在第二阶段，边缘服务器在处理任务后做出服务缓存决定。通过使用Stackelberg游戏建模，我们将整个互动过程设计为一种基于声誉的激励机制，并通过反向归纳来分析游戏双方的最佳策略。此外，我们还证明了两阶段博弈中Stackelberg平衡的存在性和唯一性，并设计了一种遗传优化算法来快速获得博弈双方的最优策略。实验结果表明，与普通的移动边缘计算方案相比，该方案不仅为边缘服务器带来了更多的收益，而且平均延迟降低了76％。