Machine Learning for Networking: Workflow, Advances and Opportunities

TL;DR

This paper reviews how machine learning techniques are applied to networking, summarizing workflows, recent advances, and future opportunities to inspire innovative research in this interdisciplinary field.

Contribution

It provides a comprehensive overview of the MLN workflow, recent advances, and potential future directions, serving as a research guideline for integrating machine learning into networking.

Findings

Summarizes the MLN workflow and design objectives.

Highlights recent advances in network design using machine learning.

Identifies new opportunities for research and community building in MLN.

Abstract

Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the net-working and distributed computing system is the key infrastructure to provide efficient computational resource for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of Machine Learning techniques for Networking (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 the machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations on 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 on 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 and motivate researchers to develop innovative algorithms, standards and frameworks.