School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Nan-An District, Chongqing 400065, China.
Chongqing Key Labs of Mobile Communications, Chongqing 400065, China.
Sensors (Basel). 2019 Nov 15;19(22):4974. doi: 10.3390/s19224974.
Due to limited computation resources of a vehicle terminal, it is impossible to meet the demands of some applications and services, especially for computation-intensive types, which not only results in computation burden and delay, but also consumes more energy. Mobile edge computing (MEC) is an emerging architecture in which computation and storage services are extended to the edge of a network, which is an advanced technology to support multiple applications and services that requires ultra-low latency. In this paper, a task offloading approach for an MEC-assisted vehicle platooning is proposed, where the Lyapunov optimization algorithm is employed to solve the optimization problem under the condition of stability of task queues. The proposed approach dynamically adjusts the offloading decisions for all tasks according to data parameters of current task, and judge whether it is executed locally, in other platooning member or at an MEC server. The simulation results show that the proposed algorithm can effectively reduce energy consumption of task execution and greatly improve the offloading efficiency compared with the shortest queue waiting time algorithm and the full offloading to an MEC algorithm.
由于车辆终端的计算资源有限，无法满足某些应用程序和服务的需求，尤其是对于计算密集型类型，这不仅会导致计算负担和延迟，还会消耗更多的能量。移动边缘计算 (MEC) 是一种新兴的架构，其中计算和存储服务扩展到网络边缘，这是支持需要超低延迟的多个应用程序和服务的先进技术。在本文中，提出了一种用于 MEC 辅助车辆编队的任务卸载方法，其中使用 Lyapunov 优化算法在任务队列稳定性条件下求解优化问题。所提出的方法根据当前任务的数据参数动态调整所有任务的卸载决策，并判断是在本地、其他编队成员还是在 MEC 服务器上执行。仿真结果表明，与最短队列等待时间算法和完全卸载到 MEC 算法相比，所提出的算法可以有效地降低任务执行的能耗，并大大提高卸载效率。
