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Demonstration: Predicting Distributions of Service Metrics
KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering. RISE SICS, Lulea, Sweden..
Ericsson Res, Gothenburg, Sweden..
Ericsson Res, Gothenburg, Sweden..
2019 (English)In: 2019 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 745-746, article id 8717915Conference paper, Published paper (Refereed)
Abstract [en]

The ability to predict conditional distributions of service metrics is key to understanding end-to-end service behavior. From conditional distributions, other metrics can be derived, such as expected values and quantiles, which are essential for assessing SLA conformance. Our demonstrator predicts conditional distributions and derived metrics estimation in real-time, using infrastructure measurements. The distributions are modeled as Gaussian mixtures whose parameters are estimated using a mixture density network. The predictions are produced for a Video-on-Demand service that runs on a testbed at KTH.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 745-746, article id 8717915
Keywords [en]
Service Engineering, Service Management, Machine Learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-254135ISI: 000469937200144ISBN: 978-3-903176-15-7 (print)OAI: oai:DiVA.org:kth-254135DiVA, id: diva2:1330333
Conference
2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019; Arlington; United States; 8 April 2019 through 12 April 2019
Note

QC 20190625

Available from: 2019-06-25 Created: 2019-06-25 Last updated: 2019-06-25Bibliographically approved

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Samani, Forough ShahabStadler, Rolf

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  • apa
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  • ieee
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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