Machine Learning Empowered Intelligent Data Center Networking (eBook)

An Introduction to the Machine Learning Empowered Intelligent Data Center Networking

Fundamentals of Machine Learning in Data Center Networks. This book reviews the common learning paradigms that are widely used in data centernetworks, and offers an introduction to data collection and data processing in data centers. Additionally, it proposes a multi-dimensional and multi-perspective solution quality assessment system called REBEL-3S. The book offers readers a solid foundation for conducting research in the field of AI-assisted data center networks.

Comprehensive Survey of AI-assisted Intelligent Data Center Networks. This book comprehensively investigates the peer-reviewed literature published in recent years. The wide range of machine learning techniques is fully reflected to allow fair comparisons. In addition, the book provides in-depth analysis and enlightening discussions on the effectiveness of AI in DCNs from various perspectives, covering flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, and network security.

Ting Wang received his Ph.D. degree in Computer Science and Engineering from Hong Kong University of Science and Technology, Hong Kong, China, in 2015. He is currently an associate professor with the Shanghai Key Laboratory of Trustworthy Computing, Software Engineering Institute, East China Normal University (ECNU), Shanghai, China. Prior to joining ECNU in 2020, he worked at the Bell Labs as a research scientist from 2015 to 2016, and at Huawei as a senior engineer from 2016 to 2020. He is currently an associate editor of IEEE Access, the editor-in-chief of IITCIB, and a technical committee member of Computer Communications, Elsevier. His research interests include SDN/NFV, data center networking, machine learning, AI-assisted intelligent networking, Internet of Things, and cloud/edge computing.

Bo Li received his Bachelor degree from the Information Engineering School, Hangzhou Dianzi University. He is currently pursuing his Master degree at Software Engineering Institute, East China Normal University, Shanghai, China. His research interests include data center networks, cloud computing, and machine learning systems.

Mingsong Chen received the B.S. and M.E. degrees from Department of Computer Science and Technology, Nanjing University, Nanjing, China, in 2003 and 2006 respectively, and the Ph.D. degree in Computer Engineering from the University of Florida, Gainesville, in 2010. He is currently a Professor with the Software Engineering Institute at East China Normal University. His research interests are in the area of cloud computing, design automation of cyber-physical systems, parallel and distributed systems, and formal verification techniques. Currently he serves as the director of MoE Engineering Research Center of Software/Hardware Codesign Technology and Application, and the vice director of technical committee of embedded systems of China Computer Federation (CCF). He is an Associate Editor of IET Computers /& Digital Techniques, and Journal of Circuits, Systems and Computers.

Shui Yu is a full Professor of School of Computer Science, University of Technology Sydney, Australia. Dr. Yu's research interest includes Security and Privacy, Networking, Big Data, and Mathematical Modelling. He has published two monographs and edited two books, more than 200 technical papers, including top journals and top conferences, such as IEEE TPDS, TC, TIFS, TMC, TKDE, TETC, ToN, and INFOCOM. Dr Yu initiated the research field of networking for big data in 2013. His h-index is 33. Dr Yu actively serves his research communities in various roles. He is currently serving the editorial boards of IEEE Communications Surveys and Tutorials, IEEE Communications Magazine, IEEE Internet of Things Journal, IEEE Communications Letters, IEEE Access, and IEEE Transactions on Computational Social Systems. He has served many international conferences as a member of organizing committee, such as publication chair for IEEE Globecom 2015, IEEE INFOCOM 2016 and 2017, TPC chair for IEEE BigDataService 2015, and general chair for ACSW 2017. Dr Yu is a final voting member for a few NSF China programs in 2017. He is a Senior Member of IEEE, a member of AAAS and ACM, the Vice Chair of Technical Committee on Big Data of IEEE Communication Society, and a Distinguished Lecturer of IEEE Communication Society.

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An Introduction to the Machine Learning Empowered Intelligent Data Center NetworkingFundamentals of Machine Learning in Data Center Networks. This book reviews the common learning paradigms that are widely used in data centernetworks, and offers an introduction to data collection and data processing in data centers. Additionally, it proposes a multi-dimensional and multi-perspective solution quality assessment system called REBEL-3S. The book offers readers a solid foundation for conducting research in the field of AI-assisted data center networks.Comprehensive Survey of AI-assisted Intelligent Data Center Networks. This book comprehensively investigates the peer-reviewed literature published in recent years. The wide range of machine learning techniques is fully reflected to allow fair comparisons. In addition, the book provides in-depth analysis and enlightening discussions on the effectiveness of AI in DCNs from various perspectives, covering flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, and network security.Provides a Broad Overview with Key Insights. This book introduces several novel intelligent networking concepts pioneered by real-world industries, such as Knowledge Defined Networks, Self-Driving Networks, Intent-driven Networks and Intent-based Networks. Moreover, it shares unique insights into the technological evolution of the fusion of artificial intelligence and data center networks, together with selected challenges and future research opportunities.