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Predicting real-tim...
Predicting real-time service-level metrics from device statistics
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- Yanggratoke, Rerngvit, 1983- (författare)
- KTH,Kommunikationsnät,ACCESS Linnaeus Centre,KTH Royal Institute of Technology, Sweden
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- Ahmed, Jawwad (författare)
- Ericsson Research, Sweden
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- Ardelius, John (författare)
- RISE,SICS,Swedish Institute of Computer Science (SICS), Sweden
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- Flinta, Christofer (författare)
- Ericsson Research, Sweden
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- Johnsson, Andreas (författare)
- Ericsson Research, Sweden
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- Gillblad, Daniel (författare)
- RISE,SICS,Swedish Institute of Computer Science (SICS), Sweden
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- Stadler, Rolf (författare)
- KTH,Kommunikationsnät,ACCESS Linnaeus Centre,Kommunikationsnät, Communication Networks,KTH Royal Institute of Technology, Sweden
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2015
- 2015
- Engelska.
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Ingår i: Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015. - : Institute of Electrical and Electronics Engineers Inc.. - 9783901882760 ; , s. 414-422
- Relaterad länk:
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https://kth.diva-por... (primary) (Raw object)
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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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Abstract
Ämnesord
Stäng
- While real-time service assurance is critical for emerging telecom cloud services, understanding and predicting performance metrics for such services is hard. In this paper, we pursue an approach based upon statistical learning whereby the behavior of the target system is learned from observations. We use methods that learn from device statistics and predict metrics for services running on these devices. Specifically, we collect statistics from a Linux kernel of a server machine and predict client-side metrics for a video-streaming service (VLC). The fact that we collect thousands of kernel variables, while omitting service instrumentation, makes our approach service-independent and unique. While our current lab configuration is simple, our results, gained through extensive experimentation, prove the feasibility of accurately predicting client-side metrics, such as video frame rates and RTP packet rates, often within 10-15% error (NMAE), also under high computational load and across traces from different scenarios.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
Nyckelord
- cloud computing
- machine learning
- network analytics
- Quality of service
- statistical learning
- video streaming
- Artificial intelligence
- Computer operating systems
- Forecasting
- Information services
- Learning systems
- Linux
- Network management
- Real time systems
- Statistics
- Cloud services
- Computational loads
- Performance metrics
- Real time service
- Server machines
- Target systems
- Video streaming services
- Distributed computer systems
- Computer Science
- Electrical Engineering
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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