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Bringing machine learning to software-defined networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bringing machine learning to software-defined networksby Zehua Guo.
Author:	Guo, Zehua.
Published:	Singapore :Springer Nature Singapore :2022.
Description:	xiii, 68 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-981-19-4874-9
ISBN:	9789811948749$q(electronic bk.)

Bringing machine learning to software-defined networks
Guo, Zehua.

Bringing machine learning to software-defined networks[electronic resource] /by Zehua Guo. - Singapore :Springer Nature Singapore :2022. - xiii, 68 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5776. - SpringerBriefs in computer science..

Chapter 1 Machine Learning for Software-Defined Networking -- Chapter 2 Deep Reinforcement Learning-based Traffic Engineering in SD-WANs -- Chapter 3 Multi-Agent Reinforcement Learning-based Controller Load Balancing in SD-WANs -- Chapter 4 Deep Reinforcement Learning-based Flow Scheduling for Power Efficiency in Data Center Networks -- Chapter 5 Graph Neural Network-based Coflow Scheduling in Data Center Networks -- Chapter 6 Graph Neural Network-based Flow Migration for Network Function Virtualization -- Chapter 7 Conclusion and Future work.

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.

ISBN: 9789811948749$q(electronic bk.)

Standard No.: 10.1007/978-981-19-4874-9doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .G86 2022

Dewey Class. No.: 006.31

Bringing machine learning to software-defined networks
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505 0 $a Chapter 1 Machine Learning for Software-Defined Networking -- Chapter 2 Deep Reinforcement Learning-based Traffic Engineering in SD-WANs -- Chapter 3 Multi-Agent Reinforcement Learning-based Controller Load Balancing in SD-WANs -- Chapter 4 Deep Reinforcement Learning-based Flow Scheduling for Power Efficiency in Data Center Networks -- Chapter 5 Graph Neural Network-based Coflow Scheduling in Data Center Networks -- Chapter 6 Graph Neural Network-based Flow Migration for Network Function Virtualization -- Chapter 7 Conclusion and Future work.
520 $a 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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