Intelligent Edge: When Wireless Network Meets Machine Learning

在过去的几年中，互联网在现代社会中的中心作用已经产生了效率和灵活性的挑战，特别是随着由于在互联网边缘的支持无线网络的移动的设备的扩散而导致的使用加剧。与此同时，机器学习技术，如深度学习和强化学习，已被应用于各种不同的应用程序，以成功地解决他们的问题。在无线网络与机器学习相遇的时代，我们的愿景是它们将相互加强，最终导致互联网的智能优势。 在设想的智能边缘中，机器学习可以用于进一步提高提供给移动的设备的服务质量。当前的移动的设备往往具有有限的计算资源和受限的电池容量。他们通常需要将计算密集型任务卸载到高性能服务器上，以便以及时和节能的方式完成任务。移动的边缘计算（MEC）被提出来通过将中等性能的边缘服务器放置在靠近移动的设备（例如，蜂窝基站）的位置来解决卸载问题。尽管MEC具有优势，但在广泛部署之前需要解决一系列技术挑战。机器学习已被用于改善MEC的性能。我们相信机器学习将在MEC中得到进一步利用，帮助为移动的设备提供令人满意的服务。此外，智能边缘中的移动的设备可以有助于实现可用的机器学习。从技术上讲，许多机器学习技术的训练和推理阶段需要大量的计算资源。AlphaGo是第一个击败职业围棋选手的计算机程序，它的诞生是最近机器学习复兴的一个重要里程碑。然而，早期版本的AlphaGo需要使用176个GPU才能达到最佳性能。机器学习所需的大量计算资源严重阻碍了其在现实生活中的应用。互联网边缘大量移动的设备的存在为基于移动设备的分布式机器学习创造了前所未有的机会。每个移动终端都是分布式机器学习中的自然计算节点。分布式移动的设备集合中的计算资源可以显著地帮助加速训练和推理过程。当无线网络遇到机器学习时，互联网的边缘将变得"智能"，因为移动的设备将以智能的方式被服务，无线网络可以帮助实现可用的机器学习。在拟议的项目中，我们将探索各种方法，为智能边缘的出现铺平道路。

Over the past years, the central role of the Internet in modern society has created challenges of efficiency and flexibility, especially as usage intensifies due to proliferation of wireless-network-enabled mobile devices at the edge of the Internet. In the meanwhile, machine learning techniques, such as deep learning and reinforcement learning, have been applied to a variety of different applications to successfully tackle their problems. In an era when wireless network meets machine learning, our vision is that they will reinforce each other, ultimately leading to an intelligent edge in the Internet.  In the envisioned intelligent edge, machine learning could be utilized to further improve the quality of the services provided to mobile devices. Current mobile devices tend to have limited computation resources and constrained battery capacity. They often need to offload computationally intensive tasks to high-performance servers in order to complete the tasks in a timely and energy-efficient manner. Mobile Edge Computing (MEC) is proposed to solve the offloading problem by placing modest-performance edge servers at locations close to mobile devices (e.g. cellular base stations). Despite the advantage of MEC, a series of technical challenges need to be tackled before it can be widely deployed. Machine learning has been used to improve the performance of MEC. We believe that machine learning will be further utilized in MEC, helping provide satisfactory services for mobile devices. Furthermore, mobile devices in the intelligent edge could contribute to the realization of usable machine learning. Technically, the training and inference phases of many machine learning techniques require a large amount of computational resources. The creation of AlphaGo, the first computer program that beats a professional Go player, is a key milestone in the recent revival of machine learning. However, the early version of AlphaGo needs to use 176 GPUs to reach its best performance. The amount of computational resources required by machine learning seriously hinders its real-life application. The existence of abundant mobile devices at the edge of the Internet literally creates an unprecedented opportunity for mobile-device-based distributed machine learning. Each mobile device is a natural computing node in distributed machine learning. The computational resources in a collection of distributed mobile devices could significantly help speed up the training and inference process. When wireless network meets machine learning, the edge of the Internet will become "intelligent" because mobile devices would be served in an intelligent manner and wireless network could help realize usable machine learning. In the proposed project, we will explore varied approaches to pave the way for the emergence of the intelligent edge.