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Published in:
Machine Learning for Software-Defined Networking Read chapter Read first chapter

2022 | OriginalPaper | Chapter

3. Multi-Agent Reinforcement Learning-Based Controller Load Balancing in SD-WANs

Author : Zehua Guo

Published in: Bringing Machine Learning to Software-Defined Networks

Publisher: Springer Nature Singapore

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Abstract

In this chapter, we introduce a dynamic controller workload balancing scheme named MARVEL using emerging MARL for switch migration. We design a DRL framework for each agent in the MARL model. The DRL-based solution takes the workload pattern in the control plane as input and generates the migration decision as the output. When training is done, the DRL agent can quickly and accurately decide how to migrate switches among the controllers.

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previous chapter Deep Reinforcement Learning-Based Traffic Engineering in SD-WANs
next chapter Deep Reinforcement Learning-Based Flow Scheduling for Power-Efficient Data Center Networks
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Metadata
Title
Multi-Agent Reinforcement Learning-Based Controller Load Balancing in SD-WANs
Author
Zehua Guo
Copyright Year
2022
Publisher
Springer Nature Singapore
Book
Bringing Machine Learning to Software-Defined Networks

Print ISBN: 978-981-19-4873-2

Electronic ISBN: 978-981-19-4874-9

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-981-19-4874-9_3

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