基于机器学习的 SDN 流量工程研究综述.

With the rising of SDN and achieving great success of machine learning methods in classification, prediction and control tasks, to find new TE technology, manage or route the traffic in the network adaptively and dynamical, ensuring QoS and improving quality of user experience (QoE) have become the focus of network research. Firstly, this paper introduced the basic structure of SDN and the research content and objectives of SDN traffic engineering. Secondly, it analyzed the application of supervised learning methods and reinforcement learning in SDN traffic engineering, and analyzed the advantages and disadvantages of existing algorithms. Finally, it summarized the future research directions and challenges. [ABSTRACT FROM AUTHOR]