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A service-agnostic ...
A service-agnostic method for predicting service metrics in real time
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- Yanggratoke, Rerngvit, 1983- (författare)
- KTH,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
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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
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- Stadler, Rolf (författare)
- KTH,RISE,SICS,KTH Royal Institute of Technology, Sweden,Nätverk och systemteknik,ACCESS Linnaeus Centre,Swedish Institute of Computer Science (SICS), Sweden
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(creator_code:org_t)
- 2017-09-13
- 2018
- Engelska.
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Ingår i: International Journal of Network Management. - : Wiley. - 1055-7148 .- 1099-1190. ; 28:2
- 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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Abstract
Ämnesord
Stäng
- We predict performance metrics of cloud services using statistical learning, whereby the behaviour of a system is learned from observations. Specifically, we collect device and network statistics from a cloud testbed and apply regression methods to predict, in real-time, client-side service metrics for video streaming and key-value store services. Results from intensive evaluation on our testbed indicate that our method accurately predicts service metrics in real time (mean absolute error below 16% for video frame rate and read latency, for instance). Further, our method is service agnostic in the sense that it takes as input operating systems and network statistics instead of service-specific metrics. We show that feature set reduction significantly improves the prediction accuracy in our case, while simultaneously reducing model computation time. We find that the prediction accuracy decreases when, instead of a single service, both services run on the same testbed simultaneously or when the network quality on the path between the server cluster and the client deteriorates. Finally, we discuss the 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 -- 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
- quality of service
- real-time network analytics
- statistical learning
- Forecasting
- Learning systems
- Regression analysis
- Statistics
- Testbeds
- Video streaming
- Design and implementations
- Mean absolute error
- Network statistics
- Performance metrics
- Prediction accuracy
- Real time network
- Real-time analytics
- Distributed computer systems
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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