Machine Learning for Networking: Workflow, Advances and Opportunities

• Published in: IEEE network Vol. 32; no. 2; pp. 92 - 99
• Main Authors: Wang, Mowei, Cui, Yong, Wang, Xin, Xiao, Shihan, Jiang, Junchen
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Computer networks; Computing time; Data models; Decision making; Distributed processing; Feature extraction; Hidden Markov models; Machine learning; Machine learning algorithms; Predictive models; Training; Workflow
• ISSN: 0890-8044; 1558-156X
• DOI: 10.1109/MNET.2017.1700200

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.