NDLI: Temperature Distribution Prediction in Data Centers for Decreasing Power Consumption by Machine Learning

Content Provider	IEEE Xplore Digital Library
Author	Tarutani, Y. Hashimoto, K. Hasegawa, G. Nakamura, Y. Tamura, T. Matsuda, K. Matsuoka, M.
Copyright Year	2015
Description	Author affiliation: Cybermedia Center, Osaka Univ., Toyonaka, Japan (Tarutani, Y.; Hasegawa, G.; Matsuda, K.; Matsuoka, M.) \|\| Grad. Sch. of Inf. Sci. & Technol., Osaka Univ., Suita, Japan (Hashimoto, K.) \|\| Grad. Sch. of Eng. Sci., Osaka Univ., Toyonaka, Japan (Nakamura, Y.) \|\| NTT Adv. Technol. Corp., Kanagawa, Japan (Tamura, T.)
Abstract	To decrease the power consumption of data centers, coordinated control of air conditioners and task assignment on servers is crucial. It takes tens of minutes for changes of operational parameters of air conditioners including outlet air temperature and volume to be actually reflected in the temperature distribution in the whole data center. Proactive control of the air conditioners is therefore required according to the predicted temperature distribution, which is highly dependent on the task assignment on the servers. In this paper, we apply a machine learning technique for predicting the temperature distribution in a data center. The temperature predictor employs regression models for describing the temperature distribution as it is predicted to be several minutes in the future, with the model parameters trained using operational data monitored at the target data center. We evaluated the performance of the temperature predictor for an experimental data center, in terms of the accuracy of the regression models and the calculation times for training and prediction. The temperature distribution was predicted with an accuracy of 0.095°C. The calculation times for training and prediction were around 1,000 seconds and 10 seconds, respectively. Furthermore, the power consumption of air conditioners was decreased by roughly 30% through proactive control based on the predicting temperature distribution.
Starting Page	635
Ending Page	642
File Size	388461
Page Count	8
File Format	PDF
ISBN	9781467395601
DOI	10.1109/CloudCom.2015.49
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-30
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature sensors Training Temperature distribution Power demand Machine learning Predictive models Temperature pridiction Data models Data center Servers Energy management
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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