Change search
CiteExportLink to record
Permanent link

Direct link
Cite
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
Automated diagnostic of virtualized service performance degradation
Show others and affiliations
2018 (English)In: IEEE/IFIP Network Operations and Management Symposium: Cognitive Management in a Cyber World, NOMS 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 1-9Conference paper, Published paper (Refereed)
Abstract [en]

Service assurance for cloud applications is a challenging task and is an active area of research for academia and industry. One promising approach is to utilize machine learning for service quality prediction and fault detection so that suitable mitigation actions can be executed. In our previous work, we have shown how to predict service-level metrics in real-time just from operational data gathered at the server side. This gives the service provider early indications on whether the platform can support the current load demand. This paper provides the logical next step where we extend our work by proposing an automated detection and diagnostic capability for the performance faults manifesting themselves in cloud and datacenter environments. This is a crucial task to maintain the smooth operation of running services and minimizing downtime. We demonstrate the effectiveness of our approach which exploits the interpretative capabilities of Self- Organizing Maps (SOMs) to automatically detect and localize different performance faults for cloud services. © 2018 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 1-9
Keywords [en]
Fault detection, Fault localization, Machine learning, Service quality, System statistics, Video streaming, Conformal mapping, Learning systems, Quality of service, Self organizing maps, Automated detection, Automated diagnostics, Cloud applications, Self organizing maps(soms), Virtualized services
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-238086DOI: 10.1109/NOMS.2018.8406234Scopus ID: 2-s2.0-85050672220ISBN: 9781538634165 (print)OAI: oai:DiVA.org:kth-238086DiVA, id: diva2:1277918
Conference
2018 IEEE/IFIP Network Operations and Management Symposium, NOMS 2018, 23 April 2018 through 27 April 2018
Note

Conference code: 137784; Export Date: 30 October 2018; Conference Paper; Funding details: VINNOVA; Funding details: 2013-03895, VINNOVA; Funding text: ACKNOWLEDGMENT This research has been supported by the Swedish Governmental Agency for Innovation Systems, VINNOVA, under grant 2013-03895.

QC 20190111

Available from: 2019-01-11 Created: 2019-01-11 Last updated: 2019-01-11Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Stadler, Rolf

Search in DiVA

By author/editor
Stadler, Rolf
By organisation
Network and Systems engineeringACCESS Linnaeus Centre
Telecommunications

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 291 hits
CiteExportLink to record
Permanent link

Direct link
Cite
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