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Predicting Distributions of Service Metrics using Neural Networks
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.
2018 (English)In: 2018 14TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM) / [ed] Salsano, S Riggio, R Ahmed, T Samak, T DosSantos, CRP, IEEE , 2018, p. 45-53Conference paper, Published paper (Refereed)
Abstract [en]

We predict the conditional distributions of service metrics, such as response time or frame rate, from infrastructure measurements in a cloud environment. From such distributions, key statistics of the service metrics, including mean, variance, or percentiles can be computed, which are essential for predicting SLA conformance or enabling service assurance. We model the distributions as Gaussian mixtures, whose parameters we predict using mixture density networks, a class of neural networks. We apply the method to a Voll service and a KY store running on our lab testbed. The results validate the effectiveness of the method when applied to operational data. In the case of predicting the mean of the frame rate or response time, the accuracy matches that of random forest, a baseline model.

Place, publisher, year, edition, pages
IEEE , 2018. p. 45-53
Series
International Conference on Network and Service Management, ISSN 2165-9605
Keywords [en]
Service Engineering, Machine Learning, Generative Models, Network Management
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-245985ISI: 000458916300006Scopus ID: 2-s2.0-85060906697ISBN: 978-3-9031-7614-0 (print)OAI: oai:DiVA.org:kth-245985DiVA, id: diva2:1295175
Conference
14th International Conference on Network and Service Management (CNSM), NOV 05-09, 2018, Rome, ITALY
Note

QC 20190311

Available from: 2019-03-11 Created: 2019-03-11 Last updated: 2019-08-20Bibliographically approved

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Samani, Forough ShahabStadler, 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