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Predicting SLA conformance for cluster-based services
UFU Federal University of Uberlândia, Brazil; KTH Royal Institute of Technology, Sweden.
Ericsson Research, Sweden.
Ericsson Research, Sweden.
Ericsson Research, Sweden.
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2017 (English)In: 2017 IFIP Networking Conference, IFIP Networking 2017 and Workshops, 2017, p. 1-2Conference paper, Published paper (Refereed)
Abstract [en]

The ability to predict conformance or violation for given Service-level Agreements (SLAs) is critical for service assurance. We demonstrate a prototype for real-time conformance prediction based on the concept of the capacity region, which abstracts the underlying ICT infrastructure with respect to the load it can carry for a given SLA. The capacity region is estimated through measurements and statistical learning. We demonstrate prediction for a key-value store (Voldemort) that runs on a server cluster located at KTH.

Place, publisher, year, edition, pages
2017. p. 1-2
Keywords [en]
Capacity Region, Feasible Region, Real-time Prediction, Service-level Agreement (SLA), Statistical Learning, Network architecture, Quality of service, Capacity regions, Feasible regions, Service Level Agreement (SLA), Forecasting
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-34627DOI: 10.23919/IFIPNetworking.2017.8264873Scopus ID: 2-s2.0-85050566272ISBN: 9783901882944 (print)OAI: oai:DiVA.org:ri-34627DiVA, id: diva2:1238745
Conference
2017 IFIP Networking Conference and Workshops, IFIP Networking 2017, 12 June 2017 through 16 June 2017
Note

 Funding details: VINNOVA; Funding details: VR, Vetenskapsrådet;

Available from: 2018-08-14 Created: 2018-08-14 Last updated: 2019-02-06Bibliographically approved

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Stadler, Rolf

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CiteExportLink to record
Permanent link

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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
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