Adaptive Power Management through Thermal Aware Workload Balancing in Internet Data Centers

The past decade witnessed the tremendous growth of online services and applications. Together with the increase of cloud computing, more and more computation are hosted by Internet data centers (IDCs). Today's IDCs are achieving significant advances in communication and computation capabilities...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems 2015-09, Vol.26 (9), p.2400-2409
Main Authors: Yao, Jianguo, Guan, Haibing, Luo, Jianying, Rao, Lei, Liu, Xue
Format: Article
Language:English
Subjects:
Adaptation models
Atmospheric modeling
Computational modeling
Control systems
Cooling
Correlation
Data centers
Data models
Internet
Mathematical models
Power consumption
Power control
Power demand
Product introduction
Servers
Workload
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Summary:The past decade witnessed the tremendous growth of online services and applications. Together with the increase of cloud computing, more and more computation are hosted by Internet data centers (IDCs). Today's IDCs are achieving significant advances in communication and computation capabilities. However, along with the increasing demand from IDC clients, power consumption for powering up and cooling these IDCs has been skyrocketing. Most existing works optimize the power consumption of either servers or Computer Room Air Conditioners (CRACs), and overlook the correlation between the power consumption of these two types of equipment. In this paper, we propose an adaptive power control method which leverages the correlation between the power consumption of servers and CRACs. To capture the workload uncertainties and thermal dynamics, we exploit Recursive-Least Square based Model Predictive Control (MPC) to solve the power control problem. Performance evaluations shows the effective power peak reduction using our approach.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2014.2353051