Developing Networks using Artificial Intelligence - Yao, Haipeng

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Résumé :
This book mainly discusses the most important issues in artificial intelligence-aided future networks, such as applying different ML approaches to investigate solutions to intelligently monitor, control and optimize networking. The authors focus on four scenarios of successfully applying machine learning in network space. It also discusses the main challenge of network traffic intelligent awareness and introduces several machine learning-based traffic awareness algorithms, such as traffic classification, anomaly traffic identification and traffic prediction. The authors introduce some ML approaches like reinforcement learning to deal with network control problem in this book. Traditional works on the control plane largely rely on a manual process in configuring forwarding, which cannot be employed for today's network conditions. To address this issue, several artificial intelligence approaches for self-learning control strategies are introduced. In addition, resource management problems are ubiquitous in the networking field, such as job scheduling, bitrate adaptation in video streaming and virtual machine placement in cloud computing. Compared with the traditional with-box approach, the authors present some ML methods to solve the complexity network resource allocation problems. Finally, semantic comprehension function is introduced to the network to understand the high-level business intent in this book. With Software-Defined Networking (SDN), Network Function Virtualization (NFV), 5th Generation Wireless Systems (5G) development, the global network is undergoing profound restructuring and transformation. However, with the improvement of the flexibility and scalability of the networks, as well as the ever-increasing complexity of networks, makes effective monitoring, overall control, and optimization of the network extremely difficult. Recently, adding intelligence to the control plane through AI&ML become a trend and a direction of network development This book's expected audience includes professors, researchers, scientists, practitioners, engineers, industry managers, and government research workers, who work in the fields of intelligent network. Advanced-level students studying computer science and electrical engineering will also find this book useful as a secondary textbook. ...

Biographie:

Haipeng Yao (IET Fellow) is a Professor in Beijing University of Posts and Telecommunications. Haipeng Yao received his Ph.D. in the Department of Telecommunication Engineering at University of Beijing University of Posts and Telecommunications in 2011. His research interests include future network architecture, network artificial intelligence, networking, space-terrestrial integrated network, network resource allocation and dedicated networks. He has published more than 200 papers in prestigious peer-reviewed journals and conferences. He won the IEEE ICC 2022 Best Paper Award, IEEE IWCMC 2019, 2021 Best Paper Award, IEEE ICCC 2020 Best Paper Award, IEEE HotICN 2020 Best Student Paper Award, authorized more than 100 national invention patents. He is a fellow of IET, a senior member of IEEE. Dr. Yao has served as an Associate Editor of IEEE Transactions on Mobile Computing, IEEE Transactions on Sustainable Computing, IEEE Network, IEEE ACCESS, and a Guest Editor of IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY and Chain Communication. He has also served as a member of the technical program committee as well as the Symposium Chair for a number of international conferences, including IEEE IWCMC 2019 Symposium Chair, IEEE TrustCom 2021 Symposium Chair and an ACM TUR-C SIGSAC2020 Publication Chair.

Tianle Mai received a Ph.D. degree in the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing. His research interests include unmanned swarm networks, future network architecture, network artificial intelligence, multi-agent system, space-terrestrial integrated network, network resource allocation and dedicated networks. He has published more than 40 papers in prestigious peer-reviewed journals and conferences. He won the IEEE ICC 2022 Best Paper Award, IEEE IWCMC 2021 Best Paper Award, IEEE ICCC 2020 Best Paper Award, IEEE HotICN 2020 Best Student Paper Award....

Sommaire:
This book mainly discusses the most important issues in artificial intelligence-aided future networks, such as applying different ML approaches to investigate solutions to intelligently monitor, control and optimize networking. The authors focus on four scenarios of successfully applying machine learning in network space. It also discusses the main challenge of network traffic intelligent awareness and introduces several machine learning-based traffic awareness algorithms, such as traffic classification, anomaly traffic identification and traffic prediction. The authors introduce some ML approaches like reinforcement learning to deal with network control problem in this book. Traditional works on the control plane largely rely on a manual process in configuring forwarding, which cannot be employed for today's network conditions. To address this issue, several artificial intelligence approaches for self-learning control strategies are introduced. In addition, resource management problems are ubiquitous in the networking field, such as job scheduling, bitrate adaptation in video streaming and virtual machine placement in cloud computing. Compared with the traditional with-box approach, the authors present some ML methods to solve the complexity network resource allocation problems. Finally, semantic comprehension function is introduced to the network to understand the high-level business intent in this book. With Software-Defined Networking (SDN), Network Function Virtualization (NFV), 5th Generation Wireless Systems (5G) development, the global network is undergoing profound restructuring and transformation. However, with the improvement of the flexibility and scalability of the networks, as well as the ever-increasing complexity of networks, makes effective monitoring, overall control, and optimization of the network extremely difficult. Recently, adding intelligence to the control plane through AI&ML become a trend and a direction of network development This book's expected audience includes professors, researchers, scientists, practitioners, engineers, industry managers, and government research workers, who work in the fields of intelligent network. Advanced-level students studying computer science and electrical engineering will also find this book useful as a secondary textbook. ...