Bringing Machine Learning to Software-Defined Networks (Paperback)

Language: English

Published by Springer Verlag, Singapore, Singapore, 2022

9811948739 / 9789811948732

Series: Book 15 of 103 - SpringerBriefs in Computer Science

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  • Softcover
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Paperback. Emerging machine learning techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of Software-Defined Networking (SDN). It will apply several innovative machine learning methods (e.g., Deep Reinforcement Learning, Multi-Agent Reinforcement Learning, and Graph Neural Network) to traffic engineering and controller load balancing in software-defined wide area networks, as well as flow scheduling, coflow scheduling, and flow migration for network function virtualization in software-defined data center networks. It helps readers reflect on several practical problems of deploying SDN and learn how to solve the problems by taking advantage of existing machine learning techniques. The book elaborates on the formulation of each problem, explains design details for each scheme, and provides solutions by running mathematical optimization processes, conducting simulated experiments, and analyzing the experimental results. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Seller Inventory # 9789811948732

Title
Bringing Machine Learning to Software-Defined Networks (Paperback)
Author
Zehua Guo
Publisher
Springer Verlag, Singapore, Singapore
Publication year
2022
Condition
new
Binding
Paperback
Language
English
ISBN 10
9811948739
ISBN 13
9789811948732
Edition
1st Edition
Series
Book 15 of 103: SpringerBriefs in Computer Science

AussieBookSeller

Truganina, VIC, Australia

5-star seller

AbeBooks seller since June 22, 2007

Shipping rates from Australia to U.S.A.

Item25 to 45 business days8 to 14 business days
First item£ 27.64£ 32.87
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