"Leveraging Deep Reinforcement Learning for Traffic Engineering: A Surv" by Yang Xiao, Jun Liu et al.
 

Leveraging Deep Reinforcement Learning for Traffic Engineering: A Survey

Document Type

Article

Publication Date

1-1-2021

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.

Identifier

85120362780 (Scopus)

Publication Title

IEEE Communications Surveys and Tutorials

External Full Text Location

https://doi.org/10.1109/COMST.2021.3102580

e-ISSN

1553877X

First Page

2064

Last Page

2097

Issue

4

Volume

23

Grant

MCM20190701

Fund Ref

Beijing University of Posts and Telecommunications

This document is currently not available here.

Share

COinS