TY - GEN
T1 - A Network Traffic Scheduling Strategy for Energy Storage Data Centers Based on SDN
AU - Peizhe, Li
AU - Zhenfeng, Xiao
AU - Zhongwei, Chen
AU - Yichao, Wang
AU - Xiangqi, Li
AU - Jin, Cheng
AU - Jingwen, Xu
AU - Yunwei, Zhang
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - With the advancement of the times, the combination of energy storage power stations and data centers (DCs) has become increasingly close. Software-defined network (SDN), as an advanced network architecture, is also gradually applied to the data center network (DCN), which plays a huge role in the network traffic scheduling. DC has the specific topology and traffic characteristics. Therefore, in the context of energy storage DC, we propose a network traffic scheduling strategy for processing long flows based on SDN in this paper. In the strategy, long flows can be divided into two parts: aggregated long flows and non-aggregated long flows. We use different algorithms to schedule these two kinds of flows. The aggregated long flows here refer to the long flows with the same source and destination. We compare our proposed strategy with the other classical traffic scheduling algorithms, the simulation results perform better in average throughput, load balancing, and link bandwidth utilization.
AB - With the advancement of the times, the combination of energy storage power stations and data centers (DCs) has become increasingly close. Software-defined network (SDN), as an advanced network architecture, is also gradually applied to the data center network (DCN), which plays a huge role in the network traffic scheduling. DC has the specific topology and traffic characteristics. Therefore, in the context of energy storage DC, we propose a network traffic scheduling strategy for processing long flows based on SDN in this paper. In the strategy, long flows can be divided into two parts: aggregated long flows and non-aggregated long flows. We use different algorithms to schedule these two kinds of flows. The aggregated long flows here refer to the long flows with the same source and destination. We compare our proposed strategy with the other classical traffic scheduling algorithms, the simulation results perform better in average throughput, load balancing, and link bandwidth utilization.
KW - aggregated long flows
KW - data center
KW - energy storage
KW - network traffic scheduling
KW - software-defined network
UR - http://www.scopus.com/inward/record.url?scp=85078196917&partnerID=8YFLogxK
U2 - 10.1109/ICCT46805.2019.8947192
DO - 10.1109/ICCT46805.2019.8947192
M3 - 会议稿件
AN - SCOPUS:85078196917
T3 - International Conference on Communication Technology Proceedings, ICCT
SP - 1413
EP - 1417
BT - 2019 IEEE 19th International Conference on Communication Technology, ICCT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Communication Technology, ICCT 2019
Y2 - 16 October 2019 through 19 October 2019
ER -