College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Sensors (Basel). 2019 Mar 24;19(6):1446. doi: 10.3390/s19061446.
This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server. Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server. In this paper, we investigate low-complexity computation offloading policies to guarantee quality of service of the MEC network and to minimize WDs' energy consumption. Specifically, both a linear programing relaxation-based (LR-based) algorithm and a distributed deep learning-based offloading (DDLO) algorithm are independently studied for MEC networks. We further propose a heterogeneous DDLO to achieve better convergence performance than DDLO. Extensive numerical results show that the DDLO algorithms guarantee better performance than the LR-based algorithm. Furthermore, the DDLO algorithm generates an offloading decision in less than 1 millisecond, which is several orders faster than the LR-based algorithm.
本文研究了移动边缘计算 (MEC) 网络，其中多个无线设备 (WD) 将其计算任务卸载到多个边缘服务器和一个云服务器。考虑到不同 WD 上的不同实时计算任务，每个任务都决定在其 WD 上本地处理，或者卸载到其中一个边缘服务器或云服务器上进行处理。在本文中，我们研究了低复杂度的计算卸载策略，以保证 MEC 网络的服务质量并最小化 WD 的能耗。具体来说，我们分别研究了基于线性规划松弛 (LR) 的算法和基于分布式深度学习的卸载 (DDLO) 算法在 MEC 网络中的应用。我们进一步提出了一种异构的 DDLO，以实现比 DDLO 更好的收敛性能。大量的数值结果表明，DDLO 算法的性能优于基于 LR 的算法。此外，DDLO 算法在不到 1 毫秒的时间内生成一个卸载决策，比基于 LR 的算法快几个数量级。
