Paper Info
Title
Flow Splitter: A Deep Reinforcement Learning-Based Flow Scheduler For Hybrid Optical-Electrical Data Center Network
Abstract
Hybrid optical-electrical switching based data center network (HOE-DCN) has been regarded as a promising architecture for the next generation data center network (DCN). To achieve traffic optimization, the main superiority of HOE-DCN is its capability to offload the long-lived 'elephant' flows by optical interconnections, and transmit the latency-sensitive 'mice' flows by electrical switching. However, most previous works identify and schedule the flows according to a fixed flow size threshold, which can hardly handle the highly dynamic network conditions in recent DCN. In order to achieve more effective flow scheduling in HOE-DCN, in this paper, we propose Flow Splitter (FS), a deep reinforcement learning (DRL) based flow scheduler which enables HOE-DCN to make instant flow scheduling according to the runtime network conditions. To train a more effective DRL agent, we upgrade the DRL method named Deep Deterministic Policy Gradient (DDPG) and propose DDPG-FS, which is capable of learning a high-performance flow scheduling policy in the complex network environment. Through simulation, we prove that our FS can significantly improve the performance of HOE-DCN. Compared with the recent flow scheduling approaches for HOE-DCN, our FS can obviously reduce the average flow complete time of arrival flows, especially the latency-sensitive mice flows.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2940445
IEEE ACCESS
Keywords
DocType
Volume
Optical switching, data center network, deep reinforcement learning, flow scheduling
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Yinan Tang143.78
HongXiang Guo22813.56
Tongtong Yuan354.12
Xiong Gao411.36
XiaoBin Hong554.30
yan li602.70
jifang qiu701.69
Yong Zuo801.35
Jian Wu93912.86