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Paper Abstract and Keywords
Presentation 2018-12-20 11:20
[Encouragement Talk] Dynamic power consumption prediction and optimization of data center by using deep learning and computational fluid dynamics
Hayato Kuwahara, Ying-Feng Hsu, Kazuhiro Matsuda, Morito Matsuoka (Osaka Univ.) NS2018-159
Abstract (in Japanese) (See Japanese page) 
(in English) 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. The error of the power consumption model built by this deep learning method was at most 8%. An algorithm with a data center optimizer (DCO) that incorporates the power consumption simulator found the optimum operation parameters of the air conditioners and optimum workload allocation that minimizes the total power consumption. In an actual implementation, the total power consumption fell within 1 second by 8% from the initial state with a uniform workload allocation. The DCO constructed in this research exhibited potential as a practical dynamic optimal task allocation management system for data centers of any size.
Keyword (in Japanese) (See Japanese page) 
(in English) DCO / 消費電力システム / CMC / / / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 371, NS2018-159, pp. 17-22, Dec. 2018.
Paper # NS2018-159 
Date of Issue 2018-12-13 (NS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
and
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)
Download PDF NS2018-159

Conference Information
Committee RCS NS  
Conference Date 2018-12-20 - 2018-12-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Onomichi City Hall 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Ad-hoc Network, Ubiquitous Network, Wireless Communication, security, Multi-Access Network 
Paper Information
Registration To NS 
Conference Code 2018-12-RCS-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Dynamic power consumption prediction and optimization of data center by using deep learning and computational fluid dynamics 
Sub Title (in English)  
Keyword(1) DCO  
Keyword(2) 消費電力システム  
Keyword(3) CMC  
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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 Osaka University (Osaka Univ.)
4th Author's Name Morito Matsuoka  
4th Author's Affiliation Osaka University (Osaka Univ.)
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Speaker
Date Time 2018-12-20 11:20:00 
Presentation Time 25 
Registration for NS 
Paper # IEICE-NS2018-159 
Volume (vol) IEICE-118 
Number (no) no.371 
Page pp.17-22 
#Pages IEICE-6 
Date of Issue IEICE-NS-2018-12-13 


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