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Paper Abstract and Keywords
Presentation 2021-04-15 13:50
Estimation of server power consumption using machine learning -- In the case of disk intensive workload --
Katsumi Fujita, Eriko Iwasa, Masashi Kaneko (NTT) NS2021-6
Abstract (in Japanese) (See Japanese page) 
(in English) A server power consumption is one of the problem in a data center. There are virtualization and DVFS approaches to address this problem. Estimating power consumption of servers is necessary for these approaches. Previous works presented server power models for CPU and memory intensive workloads. In this paper, we focused on disk intensive workloads and built the server power model by machine learning. Our method used processor performance events as input data. This model is applicable to a server power estimation for disk intensive workload. Moreover we analyzed the feature importance and found that different server power models are needed depending on the type of workloads.
Keyword (in Japanese) (See Japanese page) 
(in English) server / power consumption / machine learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 2, NS2021-6, pp. 31-36, April 2021.
Paper # NS2021-6 
Date of Issue 2021-04-08 (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 NS2021-6

Conference Information
Committee NS  
Conference Date 2021-04-15 - 2021-04-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Traffic, Network evaluation, Performance, Resource control and management, Traffic engineering, Network reliability and resilience, Network Intelligence and AI, etc. 
Paper Information
Registration To NS 
Conference Code 2021-04-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Estimation of server power consumption using machine learning 
Sub Title (in English) In the case of disk intensive workload 
Keyword(1) server  
Keyword(2) power consumption  
Keyword(3) machine learning  
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1st Author's Name Katsumi Fujita  
1st Author's Affiliation NTT (NTT)
2nd Author's Name Eriko Iwasa  
2nd Author's Affiliation NTT (NTT)
3rd Author's Name Masashi Kaneko  
3rd Author's Affiliation NTT (NTT)
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Speaker Author-1 
Date Time 2021-04-15 13:50:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2021-6 
Volume (vol) vol.121 
Number (no) no.2 
Page pp.31-36 
#Pages
Date of Issue 2021-04-08 (NS) 


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