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Automated diagnosti...
Automated diagnostic of virtualized service performance degradation
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- Ahmed, J. (författare)
- Ericsson Research, Sweden,Ericsson Res, Stockholm, Sweden.
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- Josefsson, T. (författare)
- Uppsala universitet,Institutionen för informationsteknologi
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- Johnsson, A. (författare)
- Ericsson Research, Sweden,Ericsson Res, Stockholm, Sweden.
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- Flinta, C. (författare)
- Ericsson Research, Sweden,Ericsson Res, Stockholm, Sweden.
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- Moradi, F. (författare)
- Ericsson Research, Sweden,Ericsson Res, Stockholm, Sweden.
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- Pasquini, R. (författare)
- Faculty of Computing (FACOM/UFU), Uberlândia, MG, Brazil,UFU Federal University of Uberlandia, Brazil,Univ Fed Uberlandia, FACOM, Fac Comp, Uberlandia, MG, Brazil.
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- Stadler, Rolf, Prof. (författare)
- RISE,KTH,Nätverk och systemteknik,ACCESS Linnaeus Centre,RISE Swedish Institute of Computer Science (SICS), Sweden,SICS,KTH Royal Institute of Technology, Sweden,KTH Royal Inst Technol, ACCESS Linnaeus Ctr, Stockholm, Sweden.;RISE Swedish Inst Comp Sci SICS, Stockholm, Sweden.
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Ericsson Research, Sweden Ericsson Res, Stockholm, Sweden (creator_code:org_t)
- New York : Institute of Electrical and Electronics Engineers (IEEE), 2018
- 2018
- Engelska.
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Ingår i: Proceedings 2018 IEEE/IFIP Network Operations and Management Symposium, NOMS 2018. - New York : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1-9
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Nyckelord
- 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
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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