Taking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

中文翻译：

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车辆边缘计算的网络选择和任务分担的联合优化

将移动边缘计算范式作为对车载网络的有效补充，可以使车辆获得附近的网络资源和计算能力，并满足当前大规模的车载服务需求。然而，由于车辆的强大移动性和大量任务的卸载引起的无线网络的拥塞和边缘服务器的计算资源不足，使得难以为用户提供良好的服务质量。在现有工作中，通常不考虑网络访问点选择对任务执行延迟的影响。提出了一种针对车辆任务的预分配算法，以解决由于车辆移动和边缘覆盖范围有限而导致的服务中断的问题。然后，系统模型被用来综合考虑车辆运动特性，接入点资源利用率和边缘服务器工作负载，以表征车辆任务卸载执行的总体等待时间。此外，实现了用于自动和有效的网络选择，车辆边缘计算中的任务卸载决策的自适应任务卸载策略。实验结果表明，该方法显着提高了整体任务执行性能，减少了任务卸载的时间开销。在车辆边缘计算中执行任务卸载决策。实验结果表明，该方法显着提高了整体任务执行性能，减少了任务卸载的时间开销。在车辆边缘计算中执行任务卸载决策。实验结果表明，该方法显着提高了整体任务执行性能，减少了任务卸载的时间开销。