From a global perspective, an Internet of Vehicles task offloading solution based on mobile edge computing is proposed, which satisfies the application requirements (high reliability) strictly. The average time for completing a task can be minimized with the reasonable task offloading solution. Firstly, we model the wireless network, the transmission time and the movement of vehicles. Besides, heterogeneous wireless network architecture is adopted, data centers are deployed at Small-cell Base Stations, Macro-cell Base Stations and Internet. Then considering the limitedness, heterogeneity and task diversity of resources, we utilize matching model based on combination auction to design the offloading model. Furthermore, the multi-round sequential combination auction mechanism is proposed, which equals the matching problem to the multi-dimensional grouping knapsack problem and uses dynamic programming to get the optimal match. This solution is based on virtual machine technology and voltage scaling technology in the task execution time model. Moreover, the computing resources can be measured by CPU frequency. We propose an optimization problem for the shortest average task completion time with limited resources. Finally, the effects of these parameters (such as the number of tasks per unit time, the amount of data offloaded and the number of CPU cycles) on the task execution efficiency are analyzed and compared with other algorithms by simulation experiments. Compared to existing schemes, simulation results show that the proposed algorithm can reduce system overhead and shorten task execution time effectively.

中文翻译：

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基于移动边缘计算的组合拍卖匹配模型车联网任务卸载解决方案

从全局出发，提出了一种基于移动边缘计算的车联网任务卸载解决方案，严格满足应用需求（高可靠性）。通过合理的任务卸载解决方案，可以最大限度地减少完成任务的平均时间。首先，我们对无线网络、传输时间和车辆的运动进行建模。此外，采用异构无线网络架构，数据中心部署在Small-cell基站、Macro-cell基站和Internet上。然后考虑资源的有限性、异质性和任务多样性，我们利用基于组合拍卖的匹配模型来设计卸载模型。此外，提出了多轮顺序组合拍卖机制，将匹配问题等同于多维分组背包问题，利用动态规划得到最优匹配。该解决方案基于任务执行时间模型中的虚拟机技术和电压缩放技术。此外，计算资源可以通过CPU频率来衡量。我们提出了一个在资源有限的情况下最短平均任务完成时间的优化问题。最后，通过仿真实验分析了这些参数（如单位时间内的任务数、卸载的数据量和CPU周期数）对任务执行效率的影响，并与其他算法进行了比较。与现有方案相比，仿真结果表明，该算法能够有效降低系统开销，缩短任务执行时间。