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.
