Deep Reinforcement Learning-Based Traffic Engineering in SD-WANs

Zehua Guo

doi:10.1007/978-981-19-4874-9_2

Deep Reinforcement Learning-Based Traffic Engineering in SD-WANs

Zehua Guo^*

^*Corresponding author for this work

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Citation (Scopus)

Abstract

In this chapter, we introduce ScaleDRL, which combines the control theory and DRL to achieve an efficient network control scheme for Traffic Engineering (TE). ScaleDRL employs the pinning control to select a subset of links in the network as critical links and uses a DRL algorithm to dynamically adjust link weights of the critical links. Thus, the dynamic link weight adjustment coupled with the weighted shortest path algorithm enables dynamic adjust most of the forwarding paths of flows.

Original language	English
Title of host publication	SpringerBriefs in Computer Science
Publisher	Springer
Pages	7-22
Number of pages	16
DOIs	https://doi.org/10.1007/978-981-19-4874-9_2
Publication status	Published - 2022

Publication series

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

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10.1007/978-981-19-4874-9_2

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Guo, Z. (2022). Deep Reinforcement Learning-Based Traffic Engineering in SD-WANs. In SpringerBriefs in Computer Science (pp. 7-22). (SpringerBriefs in Computer Science). Springer. https://doi.org/10.1007/978-981-19-4874-9_2

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Deep Reinforcement Learning-Based Traffic Engineering in SD-WANs

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