Leveraging Deep Reinforcement Learning for Traffic Engineering: A Survey

Yang Xiao, Jun Liu, Jiawei Wu, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

After decades of unprecedented development, modern networks have evolved far beyond expectations in terms of scale and complexity. In many cases, traditional traffic engineering (TE) approaches fail to address the quality of service (QoS) requirements of modern networks. In recent years, deep reinforcement learning (DRL) has proved to be a feasible and effective solution for autonomously controlling and managing complex systems. Massive growth in the use of DRL applications in various domains is beginning to benefit the communications industry. In this paper, we firstly provide a comprehensive overview of DRL-based TE. Then, we present a detailed literature review on applications of DRL for TE including three fundamental issues: routing optimization, congestion control, and resource management. Finally, we discuss our insights into the challenges and future research perspectives of DRL-based TE.

Original languageEnglish (US)
Pages (from-to)2064-2097
Number of pages34
JournalIEEE Communications Surveys and Tutorials
Volume23
Issue number4
DOIs
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • congestion control
  • Deep reinforcement learning
  • resource management
  • routing optimization
  • traffic engineering

Fingerprint

Dive into the research topics of 'Leveraging Deep Reinforcement Learning for Traffic Engineering: A Survey'. Together they form a unique fingerprint.

Cite this