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A platform for predicting real-time service-level metrics from device statistics
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-2680-9065
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2015 (English)In: Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, IEEE conference proceedings, 2015, 1141-1142 p.Conference paper, Published paper (Refereed)
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Text
Abstract [en]

Predicting performance metrics for cloud services is critical for real-time service assurance. We demonstrate a platform for estimating real-time service-level metrics. Statistical learning methods on device statistics are used to predict metrics for services running on these devices.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. 1141-1142 p.
Keyword [en]
Forecasting, Information services, Real time systems, Cloud services, Performance metrics, Real time service, Statistical learning methods, Network management
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-181589DOI: 10.1109/INM.2015.7140449ISI: 000380495900179Scopus ID: 2-s2.0-84942591479ISBN: 9783901882760 (print)OAI: oai:DiVA.org:kth-181589DiVA: diva2:909558
Conference
14th IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, 11 May 2015 through 15 May 2015
Note

QC 20160307

Available from: 2016-03-07 Created: 2016-02-02 Last updated: 2016-09-05Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
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  • Other locale
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Output format
  • html
  • text
  • asciidoc
  • rtf