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Demonstration :
Demonstration : Predicting distributions of service metrics
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- Samani, Forough Shahab (författare)
- KTH,Nätverk och systemteknik,KTH Royal Institute of Technology, Sweden
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- Stadler, Rolf (författare)
- KTH,RISE,SICS,KTH Royal Institute of Technology, Sweden,Nätverk och systemteknik,RISE SICS, Luleå, Sweden.
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- Johnsson, A. (författare)
- Ericsson Research, Sweden,Ericsson Res, Gothenburg, Sweden.
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- Flinta, C. (författare)
- Ericsson Research, Sweden,Ericsson Res, Gothenburg, Sweden.
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2019
- 2019
- Engelska.
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Ingår i: 2019 IFIP/IEEE Symposium on Integrated Network and Service Management, IM 2019. - : Institute of Electrical and Electronics Engineers Inc.. - 9783903176157 ; , s. 745-746
- Relaterad länk:
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https://urn.kb.se/re...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- 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
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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