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

Zehua Guo*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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.

Original languageEnglish
Title of host publicationSpringerBriefs in Computer Science
PublisherSpringer
Pages23-38
Number of pages16
DOIs
Publication statusPublished - 2022

Publication series

NameSpringerBriefs in Computer Science
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776

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Guo, Z. (2022). Multi-Agent Reinforcement Learning-Based Controller Load Balancing in SD-WANs. In SpringerBriefs in Computer Science (pp. 23-38). (SpringerBriefs in Computer Science). Springer. https://doi.org/10.1007/978-981-19-4874-9_3