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 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) |
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NS2018-18 |
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 |
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Data center |
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Deep learning |
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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 |
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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 |
6 |
Date of Issue |
2018-05-10 (NS) |
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