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| 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 |
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| 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 | 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 |
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| 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 | Author-1 |
| Date Time | 2018-12-20 11:20:00 |
| Presentation Time | 25 minutes |
| Registration for | NS |
| Paper # | NS2018-159 |
| Volume (vol) | vol.118 |
| Number (no) | no.371 |
| Page | pp.17-22 |
| #Pages | 6 |
| Date of Issue | 2018-12-13 (NS) |
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