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Online approach to performance fault localization for cloud and datacenter services
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2017 (English)In: Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network and Service Management, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 873-874Conference paper (Refereed)
Abstract [en]

Automated detection and diagnosis of the performance faults in cloud and datacenter environments is a crucial task to maintain smooth operation of different services and minimize downtime. We demonstrate an effective machine learning approach based on detecting metric correlation stability violations (CSV) for automated localization of performance faults for datacenter services running under dynamic load conditions. © 2017 IFIP.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2017. p. 873-874
Keyword [en]
Dynamic loads, Learning systems, Automated detection, Datacenter, Different services, Fault localization, Load condition, Machine learning approaches, Fault detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-216283DOI: 10.23919/INM.2017.7987390Scopus ID: 2-s2.0-85029446145ISBN: 9783901882890 OAI: oai:DiVA.org:kth-216283DiVA, id: diva2:1165602
Conference
15th IFIP/IEEE International Symposium on Integrated Network and Service Management, IM 2017, 8 May 2017 through 12 May 2017
Note

QC 20171213

Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2017-12-13Bibliographically approved

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Stadler, Rolf

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CiteExportLink to record
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Citation style
  • apa
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Language
  • de-DE
  • en-GB
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  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
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  • asciidoc
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