Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the networking and distributed computing system is the key infrastructure to provide efficient computational resources for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of MLN, which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations of their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities in networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help motivate researchers to develop innovative algorithms, standards and frameworks.

中文翻译：

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网络机器学习：工作流程、进展和机遇


最近，机器学习已被应用于各个可能的领域，以发挥其惊人的力量。长期以来，网络和分布式计算系统是为机器学习提供高效计算资源的关键基础设施。网络本身也可以从这项有前景的技术中受益。本文重点介绍MLN的应用，它不仅可以帮助解决棘手的老网络问题，还可以激发新的网络应用。在本文中，我们总结了基本工作流程来解释如何在网络领域应用机器学习技术。然后，我们对最新的代表性进展进行了选择性调查，并解释了它们的设计原理和优点。这些进步分为几个网络设计目标，并介绍了它们在 MLN 工作流程的每个步骤中如何执行的详细信息。最后，我们阐明了这一新跨学科的网络设计和社区建设的新机遇。我们的目标是提供有关机器学习网络的广泛研究指南，以帮助激励研究人员开发创新算法、标准和框架。