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Developing networks using artificial intelligence

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Developing networks using artificial intelligenceby Haipeng Yao, Chunxiao Jiang, Yi Qian.
Author:	Yao, Haipeng.
other author:	Jiang, Chunxiao.
Published:	Cham :Springer International Publishing :2019.
Description:	xi, 248 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/978-3-030-15028-0
ISBN:	9783030150280$q(electronic bk.)

Developing networks using artificial intelligence
Yao, Haipeng.

Developing networks using artificial intelligence[electronic resource] /by Haipeng Yao, Chunxiao Jiang, Yi Qian. - Cham :Springer International Publishing :2019. - xi, 248 p. :ill., digital ;24 cm. - Wireless networks,2366-1186. - Wireless networks..

Preface vii -- Acknowledgements ix -- Table of Contents xi -- Chapter 1 Introduction 1 -- Chapter 2 Intelligence-Driven Networking Architecture 13 -- Chapter 3 Intelligent Network Awareness 31 -- Chapter 4 Intelligent Network Control 79 -- Chapter 5 Intelligent Network Resource Management 151 -- Chapter 6 Intention Based Networking Management 191 -- Chapter 7 Conclusions and Future Challenges 237 -- Index 241.

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.

ISBN: 9783030150280$q(electronic bk.)

Standard No.: 10.1007/978-3-030-15028-0doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: Q335 / .Y364 2019

Dewey Class. No.: 006.3

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505 0 $a Preface vii -- Acknowledgements ix -- Table of Contents xi -- Chapter 1 Introduction 1 -- Chapter 2 Intelligence-Driven Networking Architecture 13 -- Chapter 3 Intelligent Network Awareness 31 -- Chapter 4 Intelligent Network Control 79 -- Chapter 5 Intelligent Network Resource Management 151 -- Chapter 6 Intention Based Networking Management 191 -- Chapter 7 Conclusions and Future Challenges 237 -- Index 241.
520 $a 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.
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