A Statistical Approach to Thermal Management of Data Centers Under Steady State and System Perturbations

doi:10.1198/jasa.2010.ap09236

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	A Statistical Approach to Thermal Management of Data Centers Under Steady State and System Perturbations
	Haaland, Ben 1; Min, Wanli 2; Qian, Peter Z. G.3; Amemiya, Yasuo 4
刊名	JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
	2010-09
卷号	105 期号:491 页码:1030-1041
关键词	Control boundary Cooling system Data center Forecasting Hidden Markov model Multivariate Gaussian autoregressive model Prediction Predictive modeling
ISSN号	0162-1459
DOI	10.1198/jasa.2010.ap09236
英文摘要	Temperature control for a large data center is both important and expensive. On the one hand, many of the components produce a great deal of heat, and on the other hand, many of the components require temperatures below a fairly low threshold for reliable operation. A statistical framework is proposed within which the behavior of a large cooling system can be modeled and forecast under both steady state and perturbations. This framework is based upon an extension of multivariate Gaussian autoregressive hidden Markov models (HMMs). The estimated parameters of the fitted model provide useful summaries of the overall behavior of and relationships within the cooling system. Predictions under system perturbations are useful for assessing potential changes and improvements to be made to the system. Many data centers have far more cooling capacity than necessary under sensible circumstances, thus resulting in energy inefficiencies. Using this model, predictions for system behavior after a particular component of the cooling system is shut down or reduced in cooling power can be generated. Steady-state predictions are also useful for facility monitors. System traces outside control boundaries flag a change in behavior to examine. The proposed model is fit to data from a group of air conditioners within an enterprise data center from the IT industry. The fitted model is examined, and a particular unit is found to be underutilized. Predictions generated for the system under the removal of that unit appear very reasonable. Steady-state system behavior also is predicted well.
WOS研究方向	Mathematics
语种	英语
出版者	AMER STATISTICAL ASSOC
WOS记录号	WOS:000283695300015
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/2361]
专题	上海财经大学
通讯作者	Haaland, Ben
作者单位	1.Duke Natl Univ Singapore, Grad Sch Med, Ctr Quantitat Biol & Med, Singapore 169857, Singapore; 2.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China; 3.Univ Wisconsin Madison, Dept Stat, Madison, WI 53706 USA; 4.IBM Corp, Thomas J Watson Res Ctr, Stat Anal & Forecasting, Yorktown Hts, NY 10598 USA
推荐引用方式 GB/T 7714	Haaland, Ben,Min, Wanli,Qian, Peter Z. G.,et al. A Statistical Approach to Thermal Management of Data Centers Under Steady State and System Perturbations[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,2010,105(491):1030-1041.
APA	Haaland, Ben,Min, Wanli,Qian, Peter Z. G.,&Amemiya, Yasuo.(2010).A Statistical Approach to Thermal Management of Data Centers Under Steady State and System Perturbations.JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,105(491),1030-1041.
MLA	Haaland, Ben,et al."A Statistical Approach to Thermal Management of Data Centers Under Steady State and System Perturbations".JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 105.491(2010):1030-1041.