Bringing Machine Learning to Software-Defined Networks

Dr. Zehua Guo received B.S. degree from Northwestern Polytechnical University, Xi’an, China, M.S. degree from Xidian University, Xi’an, China, and Ph.D. degree from Northwestern Polytechnical University, Xi’an, China. He is an Associate Professor at Beijing Institute of Technology, Beijing, China. He was a Research Fellow at the Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, New York, NY, USA, and a Postdoctoral Research Associate at the Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN, USA. His research interests include programmable networks (e.g., software-defined networking, network function virtualization), machine learning, and network security. He is an Associate Editor of the IEEE Systems Journal, and EURASIP Journal on Wireless Communications and Networking (Springer), an Editor of the KSII Transactions on Internet and Information Systems, and a Guest Editorof the Journal of Parallel and Distributed Computing. He was the Session Chair for the IJCAI 2021, IEEE ICC 2018,  and currently serves as the Technical Program Committee Member of Computer Communications, AAAI, IWQoS, ICC, ICCCN, and ICA3PP. He has published 58 papers in prestigious IEEE/ACM/Elsevier journals and conferences, including TON, JSAC, IJCAI, TNSM, Computer Networks, ICDCS, IWQoS, and applied/owned 14 patents. He is a Senior Member of IEEE, China Institute of Communications, and Chinese Institute of Electronics, and a Member of China Computer Federation, ACM, ACM SIGCOMM, and ACM SIGCOMM China.

Chapter 1 Machine Learning for Software-Defined Networking.- Chapter 2 Deep Reinforcement Learning-based Traffic Engineering in SD-WANs.- Chapter 3 Multi-Agent Reinforcement Learning-based Controller Load Balancing in SD-WANs.- Chapter 4 Deep Reinforcement Learning-based Flow Scheduling for Power Efficiency in Data Center Networks.- Chapter 5 Graph Neural Network-based Coflow Scheduling in Data Center Networks.- Chapter 6 Graph Neural Network-based Flow Migration for Network Function Virtualization.- Chapter 7 Conclusion and Future work.