arrow
Return

Power-Efficient Software-Defined Data Center Network

delete2021-06-15
delete4
PRE
AI
赵永 (Yong Zhao)
X
Xingwei Wang *
Q
Qiang He
B
Bo Yi
M
Min Huang
W
Wenlin Cheng
DOI:10.1109/JIOT.2020.3048524delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
The energy consumed by data centers has been growing rapidly in recent years. Among all the major contributors to the power consumption of entire data centers, data center network (DCN) can account for up to 20% of the total power consumption. In this article, we first devise a power-efficient software-defined DCN (PESD-DCN) framework, which can achieve desirable power efficiency, avoid potential link congestion, and reduce frequent device state transition. Then, we formulate the optimization problem of maximizing the radio full-utilized devices to all devices. To solve it, we propose correlation-aware flow routing (CFR) algorithm, which leverages correlation-aware flow consolidation (CFC) technique to improve energy efficiency, avoid the potential link congestion, and reduce frequent device state transition. Moreover, to further improve the DCN energy efficiency, we propose flow rerouting, link rate adaptation, and device sleeping (FLD) algorithm. Finally, simulation results demonstrate that PESD-DCN can achieve a good performance. More specifically, in comparison to the other baseline algorithms, PESD-DCN can achieve up to 79.19% energy efficiency, 67.1% decrease in switch state transition (SST), and 55.4% decrease in link state transition (LST).
Keywords:
Power demand
Data centers
Correlation
Routing
Optimization
Software
Computer science
Data center network (DCN)
device sleeping
device state transition
link rate adaptation
power-efficient routing
software-defined networking (SDN)
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Internet of Things Journal cover
IEEE Internet of Things Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W

Organization

N
northeastern university - china
Scholars:
3.1W
Papers: 2.7W
Citations: 37