论文标题
具有移动边缘计算的智能网络：动态网络调度的愿景和挑战
Intelligent networking with Mobile Edge Computing: Vision and Challenges for Dynamic Network Scheduling
论文作者
论文摘要
移动边缘计算（MEC）被认为是物联网（IoT）的一种有前途的技术。通过在设备的距离处部署边缘服务器，可以通过智能网络以相对较低的延迟提供服务和处理数据。但是，在合作和资源分配方面，广阔的边缘服务器可能面临巨大的挑战。此外，智能网络需要在分布式模式下进行在线实施。在这样的系统中，由于复杂的应用程序环境，网络调度无法遵循任何先前已知的规则。然后，统计学习成为网络调度的有前途的技术，其中边缘通过合作动态学习环境元素。预计基于学习的方法可能会减轻模型限制的缺陷，从而增强其在动态网络计划中的实际使用。在本文中，我们通过移动边缘计算研究了智能物联网网络的愿景和挑战。从系统的角度来看，关于统计学习的一些主要研究机会被列举了。
Mobile edge computing (MEC) has been considered as a promising technique for internet of things (IoT). By deploying edge servers at the proximity of devices, it is expected to provide services and process data at a relatively low delay by intelligent networking. However, the vast edge servers may face great challenges in terms of cooperation and resource allocation. Furthermore, intelligent networking requires online implementation in distributed mode. In such kinds of systems, the network scheduling can not follow any previously known rule due to complicated application environment. Then statistical learning rises up as a promising technique for network scheduling, where edges dynamically learn environmental elements with cooperations. It is expected such learning based methods may relieve deficiency of model limitations, which enhance their practical use in dynamic network scheduling. In this paper, we investigate the vision and challenges of the intelligent IoT networking with mobile edge computing. From the systematic viewpoint, some major research opportunities are enumerated with respect to statistical learning.
