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Predicting service ...
Predicting service metrics for cluster-based services using real-time analytics
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- Yanggratoke, Rerngvit, 1983- (författare)
- KTH,ACCESS Linnaeus Centre,Kommunikationsnät,Kommunikationsnät, Communication Networks,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,RISE,SICS,KTH Royal Institute of Technology, Sweden,ACCESS Linnaeus Centre,Kommunikationsnät, Communication Networks
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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 11th International Conference on Network and Service Management, CNSM 2015. - : Institute of Electrical and Electronics Engineers Inc.. - 9783901882777 ; , s. 135-143
- Relaterad länk:
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http://www.cnsm-conf...
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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
- Predicting the performance of cloud services is intrinsically hard. In this work, we pursue an approach based upon statistical learning, whereby the behaviour of a system is learned from observations. Specifically, our testbed implementation collects device statistics from a server cluster and uses a regression method that accurately predicts, in real-time, client-side service metrics for a video streaming service running on the cluster. The method is service-agnostic in the sense that it takes as input operating-systems statistics instead of service-level metrics. We show that feature set reduction significantly improves prediction accuracy in our case, while simultaneously reducing model computation time. We also discuss design and implementation of a real-time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning.
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)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- cloud computing
- machine learning
- network analytics
- Quality of service
- statistical learning
- Artificial intelligence
- Forecasting
- Learning systems
- Regression analysis
- Statistics
- Testbeds
- Video streaming
- Design and implementations
- Model computation
- Model prediction
- Prediction accuracy
- Real-time analytics
- Regression method
- Video streaming services
- Distributed computer systems
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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