1 Machine Learning for Software-Defined Networking
1.1 Introduction of Software-Defined Networking
1.1.1 Software-Defined Wide Area Network
1.1.2 Software-Defined Data Center Networks
1.2 Introduction of Machine Learning Techniques
1.2.1 Deep Reinforcement Learning 1.2.2 Multi-Agent Reinforcement Learning
1.2.3 Graph Neural Network
2 Deep Reinforcement Learning-based Traffic Engineering in SD-WANs
2.1 Introduction of Traffic Engineering
2.2 Motivation 2.2.1 Problems of Existing Solutions
2.2.2 Opportunity
2.3 Overview of ScaleDRL
2.4 Design Details of ScaleDRL
2.4.1 Pinning Control in the Offline Phase
2.4.1.1 Pinning Control
2.4.1.2 Link Selection Algorithm
2.4.2 DRL Implementation of the Online Phase
2.4.2.1 DRL Framework 2.4.2.2 Customization of Neural Networks and Interfaces
2.5 Performance Evaluation
2.5.1 Simulation Setup
2.5.2 Comparison Scheme
2.5.3 Simulation Results
2.6 Conclusion
3 Multi-Agent Reinforcement Learning-based Controller Load Balancing in SD-WANs 3.1 Introduction of Controller Load Balancing
3.2 Motivation
3.2.1 Problems of Existing Solutions
3.2.2 Opportunity
3.3 Controller Load Balancing Problem Formulation
2.3.1 Control Plane Resource Utilization Modeling
2.3.2 Control Plane Load Balancing Problem Formulation
2.3.3 Problem Complexity Analysis
3.4 Overview of MARVEL 3.5 Design Details of MARVEL
3.5.1 Training Phase
3.5.2 Working Phase
3.5.3 MARVEL Model Implementation
3.6 Performance Evaluation
3.6.1 Simulation Setup
3.6.2 Comparison Scheme 3.6.3 Simulation Results
3.7 Conclusion
4 Deep Reinforcement Learning-based Flow Scheduling for Power Efficiency in Data Center Networks
4.1 Introduction of Data Center Networks
4.1.1 Traffic Classification 4.1.2 Traffic Dynamic Analysis
4.2 Motivation
4.2.1 Problems of Existing Solutions
4.2.2 Opportunity
4.3 Problem formulation
4.3.1 Design Consi
About the Author: 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 Editor of 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.
