Machine Learning for Networking: Workflow, Advances and Opportunities

By Mowei Wang, Yong Cui, Xin Wang, Shihan Xiao, Junchen Jiang. IEEE Network, 2017

425 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/d4de528645fdfc6d954364a8e6eeeed9480ccfa2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. F. Yu
2. Tao Huang
3. Mohammad Ali Salahuddin
4. Wei Yu
5. Sara Ayoubi
6. Paul Patras
7. Renchao Xie
8. Jun-feng Xie

In Control and Systems Engineering

1. Wei Yu

Citations in the corpus

• RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimization in SDN (2019, background): “Relevant research efforts are being devoted to apply neural networks to model computer networks [5] and using such models for network optimization [6], [7], [3].”
• A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “In general, data collection can be achieved in two phases—offline and online [460].”
• A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, background): “, deep neural networks) in the network field [3], [4].”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “Networking researchers are also beginning to recognize the power and importance of deep learning, and are exploring its potential to solve problems specific to the mobile networking domain [15], [16].”
• Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, neutral): “of IoT networks [22].”
• A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective (2018, positive): “[147] reviewed a promising means of applying machine learning algorithms to address networking chal-”