Internet of Things (IoT) systems require highly scalable infrastructure to adaptively provide services to meet various performance requirements. Combining Software-Defined Networking (SDN) with Mobile Edge Cloud (MEC) technology brings more flexibility for IoT systems. We present a four-tier task processing architecture for MEC and vehicular networks, which includes processing tasks locally within a vehicle, on neighboring vehicles, on an edge cloud, and on a remote cloud. The flexible network connection is controlled by SDN. We propose a CPU resource allocation algorithm, called Partial Idle Resource Strategy (PIRS) with Vehicle to Vehicle (V2V) communications, based on Asymmetric Nash Bargaining Solution (ANBS) in Game Theory. PIRS encourages vehicles in the same location to cooperate by sharing part of their spare CPU resources. In our simulations, we adopt four applications running on the vehicles to generate workload. We compare the proposed algorithm with Non-Cooperation Strategy (NCS) and All Idle Resource Strategy (AIRS). In NCS, the vehicles execute tasks generated by the applications in their own On-Board Units (OBU), while in AIRS vehicles provide all their CPU resources to help other vehicles offloading requests. Our simulation results show that our PIRS strategy can execute more tasks on the V2V layer and lead to fewer number of task (and their length) to be offloaded to the cloud, reaching up to 28% improvement compared to NCS and up to 10% improvement compared to AIRS.

翻译：物联网系统需要高度可扩展的基础设施，以自适应地提供服务来满足各种性能需求。将软件定义网络与移动边缘云技术相结合，为物联网系统带来了更高的灵活性。我们针对移动边缘计算和车联网提出了一种四层任务处理架构，包括在车辆本地处理任务、在邻近车辆上处理任务、在边缘云上处理以及在远程云上处理。灵活的网络连接由SDN控制。我们提出了一种基于博弈论中非对称纳什讨价还价解的CPU资源分配算法，称为部分空闲资源策略，该算法支持车辆间通信。PIRS通过共享部分空闲CPU资源，激励同一位置的车辆进行协作。在模拟中，我们采用车辆上运行的四个应用来生成工作负载。我们将所提算法与非协作策略及全空闲资源策略进行了比较。在NCS中，车辆在其自有车载单元中执行应用生成的任务；而在AIRS中，车辆提供其全部CPU资源以协助其他车辆的卸载请求。仿真结果表明，我们的PIRS策略能够在V2V层执行更多任务，并减少卸载到云端的任务数量及任务长度，相比NCS性能提升最高达28%，相比AIRS性能提升最高达10%。