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Paper Abstract and Keywords
Presentation 2024-05-31 09:00
Failure Point Localization Technique with Anomaly Detection
Reiko Kondo, Takeshi Kodama, Takashi Shiraishi (FSAS TECHNOLOGIES) ICM2024-4
Abstract (in Japanese) (See Japanese page) 
(in English) As systems become more complex due to virtualization, dependencies between virtual and physical components can cause the effects of a failure to propagate to multiple components, causing many components to alert simultaneously. When such a large number of alerts occur, it is difficult to estimate the failure location from the alerts, and the failure analysis tends to be prolonged. On the other hand, various studies have been carried out to automate failure analysis by the AI technology, and one of them is an anomaly detection technology which uses machine learning to detect abnormal behavior of operation data at an early stage. If the failure location can be estimated at the stage of anomaly detection, the failure analysis can be accelerated.
In the previous report, we proposed a technique to estimate the failure location from the alert by threshold monitoring. In this paper, the verification of analyzing the failure location using anomaly detection was carried out. With the proposed technology, the operator can estimate the equipment which is likely to cause the failure in advance, and it is expected that the failure can be prevented. The proposed technique enables the operator to estimate the component which is likely to cause the failure in advance, and it is expected to prevent failures before they occur.
Keyword (in Japanese) (See Japanese page) 
(in English) failure point detection / scoring / operations / distributed storage / anomaly detection / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 53, ICM2024-4, pp. 15-20, May 2024.
Paper # ICM2024-4 
Date of Issue 2024-05-23 (ICM) 
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 ICM2024-4

Conference Information
Committee ICM IPSJ-IOT IPSJ-CSEC  
Conference Date 2024-05-30 - 2024-05-31 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ICM 
Conference Code 2024-05-ICM-IOT-CSEC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Failure Point Localization Technique with Anomaly Detection 
Sub Title (in English)  
Keyword(1) failure point detection  
Keyword(2) scoring  
Keyword(3) operations  
Keyword(4) distributed storage  
Keyword(5) anomaly detection  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Reiko Kondo  
1st Author's Affiliation FSAS TECHNOLOGIES INC. (FSAS TECHNOLOGIES)
2nd Author's Name Takeshi Kodama  
2nd Author's Affiliation FSAS TECHNOLOGIES INC. (FSAS TECHNOLOGIES)
3rd Author's Name Takashi Shiraishi  
3rd Author's Affiliation FSAS TECHNOLOGIES INC. (FSAS TECHNOLOGIES)
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Speaker Author-1 
Date Time 2024-05-31 09:00:00 
Presentation Time 25 minutes 
Registration for ICM 
Paper # ICM2024-4 
Volume (vol) vol.124 
Number (no) no.53 
Page pp.15-20 
#Pages 6 
Date of Issue 2024-05-23 (ICM) 

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