Abstract

Driven by the explosion transmission and computation requirement in 5G vehicular networks, mobile edge computing (MEC) attracts more attention than centralized cloud computing. The advantage of MEC is to provide a large amount of computation and storage resources to the edge of networks so as to offload computation-intensive and delay-sensitive applications from vehicle terminals. However, according to the mobility of vehicle terminals and the time varying traffic load, the optimal task offloading decisions is crucial. In this paper, we consider the uplink transmission from vehicles to road side units in the vehicular network. A dynamic task offloading decision for flexible subtasks is proposed to minimize the utility, which includes energy consumption and packet drop rate. Furthermore, a computation resource allocation scheme is introduced to allocate the computation resources of MEC server due to the differences in the computation intensity and the transmission queue of each vehicle. Consequently, a Lyapunov-based dynamic offloading decision algorithm is proposed, which combines the dynamic task offloading decision and computation resource allocation, to minimize the utility function while ensuring the stability of the queue. Finally, simulation results demonstrate that the proposed algorithm could achieve a significant improvement in the utility of vehicular networks compared with comparison algorithms.

中文翻译：

---

车载网络中移动边缘计算的节能卸载决策

摘要

在5G车载网络中爆炸传输和计算需求的推动下，移动边缘计算（MEC）比集中式云计算吸引了更多关注。MEC的优点是为网络边缘提供了大量的计算和存储资源，以减轻车辆终端对计算密集型和时延敏感的应用程序的负担。但是，根据车辆终端的移动性和随时间变化的交通负载，最佳的任务卸载决策至关重要。在本文中，我们考虑车辆网络中从车辆到路边单元的上行链路传输。提出了用于灵活子任务的动态任务卸载决策，以最大程度地降低实用性，其中包括能耗和丢包率。此外，引入了计算资源分配方案，由于各车辆的计算强度和传输队列的差异，分配了MEC服务器的计算资源。因此，提出了一种基于Lyapunov的动态卸载决策算法，该算法将动态任务卸载决策与计算资源分配相结合，在保证队列稳定性的同时，最大限度地降低了效用函数。最后，仿真结果表明，与比较算法相比，所提算法在车载网络的实用性上有明显的提高。它结合了动态任务卸载决策和计算资源分配，以最大程度地减少实用程序功能，同时确保队列的稳定性。最后，仿真结果表明，与比较算法相比，所提算法在车载网络的实用性上有明显的提高。它结合了动态任务卸载决策和计算资源分配，以最大程度地减少实用程序功能，同时确保队列的稳定性。最后，仿真结果表明，与比较算法相比，所提算法在车载网络的实用性上有明显的提高。