Paper Info
Title
GreenTE.ai: Power-Aware Traffic Engineering via Deep Reinforcement Learning
Abstract
Power-aware traffic engineering via coordinated sleeping is usually formulated into Integer Programming problems, which are generally NP-hard with unbounded computation time for large-scale networks. This results in delayed control decision making in dynamic network environments. Motivated by advances in deep Reinforcement Learning, we consider building intelligent systems that learn to adaptively change router/switch's power state according to changing network conditions. Neural network's forward propagation can greatly speed up power on/off decision making. Generally, conducting RL requires a learning agent to iteratively explore and perform the "good" actions based on the feedback from the environment. By coupling Software-Defined Networking for performing centrally calculated actions to the environment and In-band Network Telemetry for collecting feedback from the environment, we develop GreenTE.ai, a closed-loop control/training system to automate power-aware traffic engineering. Furthermore, we propose novel techniques to enhance the learning ability and reduce the learning complexity. With both energy efficiency and traffic load balancing considered, GreenTE.ai can generate reasonable power saving actions within 276ms under a network testbed of 11 software P4 switches.
Year
DOI
Venue
2021
10.1109/IWQOS52092.2021.9521281
2021 IEEE/ACM 29TH INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE (IWQOS)
DocType
ISSN
Citations 
Conference
1548-615X
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Tian Pan12111.90
Xiaoyu Peng200.34
Qianqian Shi300.34
Zizheng Bian441.46
Xingchen Lin542.47
Enge Song600.34
Fuliang Li7187.12
Yang Xu818726.15
tao915330.30