Machine Learning in Software Defined Network

Abstract

As a new network architecture, software defined network (SDN) separates the control plane from the forwarding plane which enables administrators to define and control the network through the method of software programming, provides a new research direction for the next generation of network architecture. At the same time, the machine learning technology has been developed rapidly in recent years and some studies have begun to introduce machine learning methods into SDN to improve the efficiency of network management and conformity, or to solve problems that cannot be solved easily by traditional methods. The paper analyses, summarizes and introduces these researches which used the supervised learning, unsupervised learning or semi-supervised learning methods to solve some specific problems on SDN, and it will help later researchers understand the filed more quickly and promote the development of the machine learning technology in SDN.