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Research Papers

Fast and Accurate Evaluation of Cooling in Data Centers

[+] Author and Article Information
Harshad Bhagwat

TCS Innovation Labs-TRDDC,
Tata Consultancy Services Limited,
Pune 411013, India
e-mail: harshad.bhagwat@tcs.com

Umesh Singh

TCS Innovation Labs-TRDDC,
Tata Consultancy Services Limited,
Pune 411013, India
e-mail: u.singh@tcs.com

Anirudh Deodhar

TCS Innovation Labs-TRDDC,
Tata Consultancy Services Limited,
Pune 411013, India
e-mail: anirudh.deodhar@tcs.com

Amarendra Singh

TCS Innovation Labs-TRDDC,
Tata Consultancy Services Limited,
Pune 411013, India
e-mail: amarendra.singh@tcs.com

Anand Sivasubramaniam

Department of Computer Science
and Engineering,
The Pennsylvania State University,
State College, PA 16802
e-mail: anand.sivasubramaniam@tcs.com

1The work was done during the author's sabbatical at Tata Consultancy Services.

Contributed by the Electronic and Photonic Packaging Division of ASME for publication in the JOURNAL OF ELECTRONIC PACKAGING. Manuscript received July 24, 2013; final manuscript received August 14, 2014; published online October 6, 2014. Assoc. Editor: Yogendra Joshi.

J. Electron. Packag 137(1), 011003 (Oct 06, 2014) (9 pages) Paper No: EP-13-1092; doi: 10.1115/1.4028315 History: Received July 24, 2013; Revised August 14, 2014

Cooling is a major component in the enormous energy consumption in data centers. Accurate evaluation of cooling inside a data center forms the backbone of all the attempts for improving cooling efficiency. Models based on computational fluid dynamics (CFD) are typically used for accurate evaluation, but have a drawback of high computation time. This paper presents a novel thermal predictor to evaluate data center cooling in seconds. The key idea is to extract information from a single instance of CFD simulation using metrics called as influence indices to build the fast thermal predictor. Then, this predictor can evaluate the cooling for altered operation of data center with comparable accuracy in seconds without the need for repetitive CFD simulations. This paper demonstrates the accuracy of the thermal predictor by comparing with CFD simulations for a sample, but realistic data center. The fast thermal predictor then successfully passed more challenging tests in a real production data center and proved its practical utility. The results of the thermal predictor compared with measurements carried out in the production data center are also presented. This fast thermal predictor is an important milestone in the development of a method for model-based real time control of data center cooling.

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References

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Figures

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Fig. 1

Layout of the sample data center

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Fig. 2

Layout of the production data center

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Fig. 3

Comparison of prediction of temperatures at rack inlets and CRAC returns from CFD and thermal predictor for all the ten cases of the sample data center

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Fig. 4

Comparison of prediction of temperatures at rack inlets and CRAC returns from CFD and thermal predictor for case 9 of the sample data center

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Fig. 5

Comparison of prediction of temperatures on a horizontal plane at height 1 m from CFD and thermal predictor for case 1 and case 9 of the sample data center

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Fig. 6

Comparison of predictions of temperatures from thermal predictor with measurements at two rack inlets in production data center

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