Bringing machine learning to software-defined networks

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

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 me...

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Bibliographic Details
Main Author: Guo, Zehua (Author)
Format: eBook
Language:English
Published: Singapore : Springer, [2022]
Series:SpringerBriefs in computer science.
Subjects:
Machine learning.
Software-defined networking (Computer network technology)
Machine learning
Electronic books.
Online Access:Click for online access
Table of Contents:
  • 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.

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