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Predicting Real-tim...
Predicting Real-time Service-level Metrics from Device Statistics
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- Yanggratoke, Rerngvit, 1983- (author)
- KTH,ACCESS Linnaeus Centre,Kommunikationsnät, Communication Networks
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- Ahmed, Jawwad (author)
- Ericsson Research, Sweden
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- Ardelius, John (author)
- RISE,SICS
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- Flinta, Christofer (author)
- Ericsson Research, Sweden
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- Johnsson, Andreas (author)
- Ericsson Research, Sweden
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- Gillblad, Daniel (author)
- RISE,SICS
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- Stadler, Rolf (author)
- KTH,ACCESS Linnaeus Centre,Kommunikationsnät, Communication Networks
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(creator_code:org_t)
- Kista, Sweden : KTH Royal Institute of Technology, 2014
- English 9 s.
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Series: SICS Technical Report, 1100-3154 ; 2014:05
- Related links:
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https://kth.diva-por... (primary) (Raw object)
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https://ri.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://urn.kb.se/re...
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Abstract
Subject headings
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- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- 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 -- Telekommunikation (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- Quality of service
- cloud computing
- network analytics
- statistical learning
- machine learning
- video streaming
Publication and Content Type
- vet (subject category)
- rap (subject category)
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