Analysis of promising approaches and research on traffic flow classification for maintaining QoS by ML methods in SDN networks

Abstract

One of the most important tasks that exist in modern networks is to maintain the Quality-of-Service QoS at the appropriate level which can be achieved by applying various traffic management mechanisms. In order to maintain the QoS parameters in the proper state, you need to know the types of traffic passing through the network. Given high-tech and high-performance networks such as SDN networks, traffic classification by conventional methods becomes almost impossible. Data mining methods, including Machine Learning methods, come to the rescue. The article analyzes the main promising approaches to real-time traffic classification for maintaining QoS in SDN networks by ML methods as well as provides a comparative overview of the most outstanding works in this field.