Bringing Machine Learning to Software-Defined Networks

Language: English

Published by Springer Nature Singapore, 2022

9811948739 / 9789811948732

Series: Book 15 of 103 - SpringerBriefs in Computer Science

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Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.…

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Title
Bringing Machine Learning to Software-Defined Networks
Author
Zehua Guo
Publisher
Springer Nature Singapore
Publication year
2022
Condition
Hervorragend
Binding
Soft cover
Language
English
ISBN 10
9811948739
ISBN 13
9789811948732
Series
Book 15 of 103: SpringerBriefs in Computer Science
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Buchpark

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