TY - JOUR
T1 - Leveraging Deep Reinforcement Learning for Traffic Engineering
T2 - A Survey
AU - Xiao, Yang
AU - Liu, Jun
AU - Wu, Jiawei
AU - Ansari, Nirwan
N1 - Funding Information:
This work was supported in part by the MoE-CMCC Artificial Intelligence Project under Grant MCM20190701, and in part by the BUPT Excellent Ph.D. Students Foundation under Grant CX2021112.
Publisher Copyright:
© 1998-2012 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Deep reinforcement learning
KW - congestion control
KW - resource management
KW - routing optimization
KW - traffic engineering
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U2 - 10.1109/COMST.2021.3102580
DO - 10.1109/COMST.2021.3102580
M3 - Article
AN - SCOPUS:85120362780
SN - 1553-877X
VL - 23
SP - 2064
EP - 2097
JO - IEEE Communications Surveys and Tutorials
JF - IEEE Communications Surveys and Tutorials
IS - 4
ER -