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Performance Analytics by Means of the M5P Machine Learning Algorithm
Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
2019 (English)In: Proceedings of the 31st International Teletraffic Congress, ITC 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 104-105Conference paper, Published paper (Refereed)
Abstract [en]

Machine Learning (ML) has shown its capability to analyse, classify, and make predictions based on large data sets. Network performance analysis and evaluation focuses on finding and expressing qualitative, quantitative and formal relationships between performance-related parameters, with specific interest in asymptotic behaviours. This work introduces the notion of performance analytics as performance modeling with help of ML. In particular, it demonstrates the applicablibility of the ML algorithm M5P to such performance analytics, as the parameters of the generated model trees allow for identifying approximations together the applicable parameter sub-spaces in a straightforward approach. We present a set of examples with focus on post-analysis of analytically obtained performance results for asymptotic behaviour. © 2019 ITC Press.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019. p. 104-105
Keywords [en]
approximation formulae, model tree, multivariate analysis, performance evaluation, Approximation algorithms, Asymptotic analysis, Classification (of information), Forestry, Learning algorithms, Multivariant analysis, Parameter estimation, Trees (mathematics), Asymptotic behaviour, Large datasets, Model trees, Multi variate analysis, Network performance analysis, Performance Model, Machine learning
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:bth-18937DOI: 10.1109/ITC31.2019.00023Scopus ID: 2-s2.0-85074777929ISBN: 9780988304574 (print)OAI: oai:DiVA.org:bth-18937DiVA, id: diva2:1371975
Conference
31st International Teletraffic Congress, ITC, Budapest, 27 August 2019 through 29 August 2019
Note

Funding details: d-nr 2014-0032; Funding text 1: ACKNOWLEDGEMENTS The co-funding of the “BigData@BTH” project by the Swedish KKS Foundation (d-nr 2014-0032) is gratefully acknowledged.

Available from: 2019-11-21 Created: 2019-11-21 Last updated: 2019-11-21Bibliographically approved

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Fiedler, Markus

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • 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