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.
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.
