Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • 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
Change Point Detection in Clustered Network Performance Indicators
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0003-3461-7079
2022 (English)In: Proceedings of the IEEE/IFIP Network Operations and Management Symposium 2022: Network and Service Management in the Era of Cloudification, Softwarization and Artificial Intelligence, NOMS 2022 / [ed] Varga P., Granville L.Z., Galis A., Godor I., Limam N., Chemouil P., Francois J., Pahl M.-O., Institute of Electrical and Electronics Engineers (IEEE), 2022Conference paper, Published paper (Refereed)
Abstract [en]

The detailed performance characteristics of networking equipment is to a large extent a function of the software that controls the underlying hardware components. Most networking equipment is regularly updated with new software versions. By studying performance changes related to such changes in software, it is possible to identify particular software versions that affect the performance of the system. Consequently, having automated methods for detecting changes in network equipment performance is crucial. In this work we study the change point detection problem arising when the placement in time of software updates is known a priori, but the presence of any performance implications on any of the thousands of performance indicators that can be collected is unknown. The ability to improve the automated detection of such change points by clustering the monitored systems according to the set of collected indicators has not been fully evaluated. We here report our experience with employing clustering, together with a bootstrap-based change point detection, across a range of performance indicators. We evaluate four variations of clustering approaches, and demonstrate the resulting improvement in change point detection sensitivity. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022.
Series
IEEE IFIP Network Operations and Management Symposium
Keywords [en]
bootstrapping; Change points; clustering; NFV
National Category
Information Systems, Social aspects Information Systems, Social aspects
Identifiers
URN: urn:nbn:se:kau:diva-91686DOI: 10.1109/NOMS54207.2022.9789781ISI: 000851572700037Scopus ID: 2-s2.0-85133171506OAI: oai:DiVA.org:kau-91686DiVA, id: diva2:1691967
Conference
2022 IEEE/IFIP Network Operations and Management Symposium, NOMS 2022
Available from: 2022-08-31 Created: 2022-08-31 Last updated: 2025-02-17Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Garcia, Johan

Search in DiVA

By author/editor
Garcia, Johan
By organisation
Department of Mathematics and Computer Science (from 2013)
Information Systems, Social aspectsInformation Systems, Social aspects

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 318 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • apa.csl
  • 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