Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Automated diagnostic of virtualized service performance degradation
Visa övriga samt affilieringar
2018 (Engelska)Ingår i: IEEE/IFIP Network Operations and Management Symposium: Cognitive Management in a Cyber World, NOMS 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, s. 1-9Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers Inc. , 2018. s. 1-9
Nyckelord [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
Nationell ämneskategori
Telekommunikation
Identifikatorer
URN: urn:nbn:se:kth:diva-238086DOI: 10.1109/NOMS.2018.8406234Scopus ID: 2-s2.0-85050672220ISBN: 9781538634165 (tryckt)OAI: oai:DiVA.org:kth-238086DiVA, id: diva2:1277918
Konferens
2018 IEEE/IFIP Network Operations and Management Symposium, NOMS 2018, 23 April 2018 through 27 April 2018
Anmärkning

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

Tillgänglig från: 2019-01-11 Skapad: 2019-01-11 Senast uppdaterad: 2019-01-11Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Personposter BETA

Stadler, Rolf

Sök vidare i DiVA

Av författaren/redaktören
Stadler, Rolf
Av organisationen
Nätverk och systemteknikACCESS Linnaeus Centre
Telekommunikation

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 291 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf