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Predicting SLA conformance for cluster-based services using distributed analytics
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Communication Networks. (Kommunikationsnät, Communication Networks)ORCID iD: 0000-0002-2680-9065
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2016 (English)In: Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, IEEE conference proceedings, 2016, p. 848-852Conference paper, Published paper (Refereed)
Abstract [en]

Service assurance for the telecom cloud is a challenging task and is continuously being addressed by academics and industry. One promising approach is to utilize machine learning to predict service quality in order to take early mitigation actions. In previous work we have shown how to predict service-level metrics, such as frame rate for a video application on the client side, from operational data gathered at the server side. This gives the service provider early indications on whether the platform can support the current load demand. This paper extends previous work by addressing scalability issues for cluster-based services. Operational data being generated in large volumes, from several sources, and at high velocity puts strain on computational and communication resources. We propose and evaluate a distributed machine learning system based on the Winnow algorithm to tackle scalability issues, and then compare the new distributed solution with the previously proposed centralized solution. We show that network overhead and computational execution time is substantially reduced while maintaining high prediction accuracy making it possible to achieve real-time service quality predictions in large systems.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. p. 848-852
Keywords [en]
Artificial intelligence, Forecasting, Information services, Quality of service, Real time systems, Scalability, Communication resources, Computational execution time, Distributed machine learning, Distributed solutions, Prediction accuracy, Real time service, Service assurance, Video applications, Learning systems
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-197198DOI: 10.1109/NOMS.2016.7502913ISI: 000389830100124Scopus ID: 2-s2.0-84979784139ISBN: 9781509002238 (print)OAI: oai:DiVA.org:kth-197198DiVA, id: diva2:1051184
Conference
2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016, 25 April 2016 through 29 April 2016
Note

QC 20161201

Available from: 2016-12-01 Created: 2016-11-30 Last updated: 2017-01-24Bibliographically approved

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Publisher's full textScopushttp://noms2016.ieee-noms.org/

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Stadler, Rolf

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