QOS-Aware Flow Control for Power-Efficient Data Center Networks with Deep Reinforcement Learning
Penghao Sun, Zehua Guo, Sen Liu, Julong Lan, Yuxiang Hu
Abstract
Reducing the power consumption and maintaining the Flow Completion Time (FCT) for the Quality of Service (QoS) of applications in Data Center Networks (DCNs) are two major concerns for data center operators. However, existing works either fail in guaranteeing the QoS due to the neglect of the FCT constraints or achieve a less satisfying power efficiency. In this paper, we propose SmartFCT, which employs Software-Defined Networking (SDN) coupled with the Deep Reinforcement Learning (DRL) to improve the power efficiency of DCNs and guarantee the FCT. The DRL agent can generate a dynamic policy to consolidate traffic flows into fewer active switches in the DCN for power efficiency, and the policy also leaves different margins in different active links and switches to avoid FCT violation of unexpected short bursts of flows. Simulation results show that with similar FCT guarantee, SmartFCT can save 8% more of the power consumption compared to the state-of-the-art solutions.
BibTeX
@inproceedings{icassp2020_qosawareflowcont,
title = {QOS-Aware Flow Control for Power-Efficient Data Center Networks with Deep Reinforcement Learning},
author = {Penghao Sun and Zehua Guo and Sen Liu and Julong Lan and Yuxiang Hu},
booktitle = {ICASSP 2020},
year = {2020}
}