DROM: Optimizing the Routing in Software-Defined Networks with Deep Reinforcement Learning

Changhe Yu, Julong Lan, Zehua Guo, Yuxiang Hu

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

This paper proposes DROM, a deep reinforcement learning mechanism for Software-Defined Networks (SDN) to achieve a universal and customizable routing optimization. DROM simplifies the network operation and maintenance by improving the network performance, such as delay and throughput, with a black-box optimization in continuous time. We evaluate the DROM with experiments. The experimental results show that DROM has the good convergence and effectiveness and provides better routing configurations than existing solutions to improve the network performance, such as reducing the delay and improving the throughput.

Original languageEnglish (US)
Article number8502806
Pages (from-to)64533-64539
Number of pages7
JournalIEEE Access
Volume6
DOIs
StatePublished - 2018

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China for Innovative Research Groups under Grant 61521003, in part by the National Natural Science Foundation of China under Grant 61502530, and in part by the National High Technology Research and Development Program (863 Program) of China under Grant 2015AA016102.

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Deep reinforcement learning
  • routing optimization
  • software-defined networking

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