Nowadays, a new paradigm named mobile edge computing (MEC) is capable of supplying some cloud-like functions at the edges of wireless networks, which enables vehicles to offload the computation intensive tasks on MEC servers with low latency. However, new challenges posed by the complex network environment and the mobility of vehicles are usually not covered by traditional offloading schemes. To solve such problems, we propose a heuristic task migration computation offloading (TMCO) scheme. Compared with traditional ones, TMCO can dynamically choose suitable places to offload the tasks for moving vehicles within deadline. For this purpose, the mobility of vehicle and strict delay deadline are considered comprehensively. We use hash table to store the number of tasks on the corresponding server and use random function to simulate the probability of task offloading. In terms of latency, experimental results suggest that the performance of TMCO is on average 10% higher than that of traditional full offloading schemes.

中文翻译：

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车载网络中移动边缘计算的低延迟任务迁移计算卸载

如今，一种名为移动边缘计算 (MEC) 的新范式能够在无线网络的边缘提供一些类似云的功能，这使车辆能够以低延迟卸载 MEC 服务器上的计算密集型任务。然而，复杂的网络环境和车辆的移动性带来的新挑战通常无法被传统的卸载方案所涵盖。为了解决这些问题，我们提出了一种启发式任务迁移计算卸载（TMCO）方案。与传统的相比，TMCO可以动态地选择合适的地点来在期限内卸载移动车辆的任务。为此，综合考虑了车辆的机动性和严格的延迟期限。我们使用哈希表来存储相应服务器上的任务数量，并使用随机函数来模拟任务卸载的概率。在延迟方面，实验结果表明，TMCO 的性能平均比传统的完全卸载方案高 10%。