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Demonstration: Predicting distributions of service metrics
KTH Royal Institute of Technology, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0001-6039-8493
Ericsson Research, Sweden.
Ericsson Research, Sweden.
2019 (English)In: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 745-746Conference 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 realtime, 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 Inc. , 2019. p. 745-746
Keywords [en]
Machine Learning, Service Engineering, Service Management, Forecasting, Learning systems, Video on demand, Conditional distribution, End-to-end service, Expected values, Gaussian mixtures, Mixture density, Video on demand services, Telecommunication services
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-39271Scopus ID: 2-s2.0-85067047473ISBN: 9783903176157 (print)OAI: oai:DiVA.org:ri-39271DiVA, id: diva2:1334744
Conference
2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019, 8 April 2019 through 12 April 2019
Available from: 2019-07-03 Created: 2019-07-03 Last updated: 2019-07-03Bibliographically approved

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Stadler, Rolf

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Output format
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