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Paper Abstract and Keywords
Presentation 2018-05-17 11:15
Dynamic power consumption prediction of data center by using deep learning and computational fluid dynamics
Hayato Kuwahara, Ying-Feng Hsu (Osaka Univ.), Kazuhiro Matsuda (NTT-AT), Morito Matsuoka (Osaka Univ.) NS2018-18
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, simply by using computational fluid dynamics (CFD) and a power consumption model incorporating each piece of equipment including servers and air conditioners, we built a power consumption simulator to predict the total power consumption of a data center that can have any device configuration, without having
to learn the entire data center in advance. Specifically, we built the server power consumption model by using a set of CPU usage rate of the server, intake air temperature, and exhaust wind speed on the back of the server measured in the data center. We assumed that the exhaust wind speed at the back of the server is a first approximation to the rotation speed of the server fan and is the sum of the exhaust wind speed by the server fan and the air speed distribution by air conditioning calculated by CFD simulation and predicted the server power consumption with the power consumption model. This power consumption simulation was applied to the test bed of data center. We compared the prediction result of the power consumption when CPU utilization rate was uniform and the actual power consumption. As a result, at most a simulation error is suppressed to 10 % or less. From these results, we found that it can be effective for realizing a practical dynamic optimum task allocation management system.
Keyword (in Japanese) (See Japanese page) 
(in English) Data center / Deep learning / CFD / / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 38, NS2018-18, pp. 19-24, May 2018.
Paper # NS2018-18 
Date of Issue 2018-05-10 (NS) 
ISSN Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee NS  
Conference Date 2018-05-17 - 2018-05-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Yokohama City Education Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) High level protocol, Networking technologies (IP and high-layer routing/filtering, Multicast, Quality/Routing control), IP network application technologies (P2P, P4P, Overlay, SIP, NGN), Network system related technologies (System configuration, Interface, Architecture, Hardware/Software/Middleware), etc. 
Paper Information
Registration To NS 
Conference Code 2018-05-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Dynamic power consumption prediction of data center by using deep learning and computational fluid dynamics 
Sub Title (in English)  
Keyword(1) Data center  
Keyword(2) Deep learning  
Keyword(3) CFD  
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1st Author's Name Hayato Kuwahara  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Ying-Feng Hsu  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Kazuhiro Matsuda  
3rd Author's Affiliation NTT Advanced Technology Corporation (NTT-AT)
4th Author's Name Morito Matsuoka  
4th Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2018-05-17 11:15:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2018-18 
Volume (vol) vol.118 
Number (no) no.38 
Page pp.19-24 
#Pages
Date of Issue 2018-05-10 (NS) 


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