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Title
Bringing machine learning to software-defined networks / Zehua Guo.
ISBN
9789811948749 (electronic bk.)
9811948747 (electronic bk.)
9789811948732
9811948739
Published
Singapore : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource (xiii, 68 pages) : illustrations.
Item Number
10.1007/978-981-19-4874-9 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed October 14, 2022).
Series
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.
