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  3. Machine learning

Machine Learning Empowered Intelligent Data Center Networking

- Evolution, Challenges and Opportunities
Af: Shui Yu, Ting Wang, Bo Li, Mingsong Chen Engelsk Paperback

Machine Learning Empowered Intelligent Data Center Networking

- Evolution, Challenges and Opportunities
Af: Shui Yu, Ting Wang, Bo Li, Mingsong Chen Engelsk Paperback
Tjek vores konkurrenters priser

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.


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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.


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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 112
ISBN-13: 9789811973949
Indbinding: Paperback
Udgave:
ISBN-10: 9811973946
Kategori: Machine learning
Udg. Dato: 22 feb 2023
Længde: 0mm
Bredde: 155mm
Højde: 235mm
Forlag: Springer Verlag, Singapore
Oplagsdato: 22 feb 2023
Forfatter(e): Shui Yu, Ting Wang, Bo Li, Mingsong Chen
Forfatter(e) Shui Yu, Ting Wang, Bo Li, Mingsong Chen

Kategori Machine learning

ISBN-13 9789811973949

Sprog Engelsk

Indbinding Paperback

Sider 112

Udgave

Længde 0mm

Bredde 155mm

Højde 235mm

Udg. Dato 22 feb 2023

Oplagsdato 22 feb 2023

Forlag Springer Verlag, Singapore

Kategori sammenhænge