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A service-agnostic method for predicting service metrics in real time
KTH Royal Institute of Technology, Sweden.
Ericsson Research, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS.
Ericsson Research, Sweden.
Show others and affiliations
2018 (English)In: International Journal of Network Management, ISSN 1055-7148, E-ISSN 1099-1190, Vol. 28, no 2, article id e1991Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
2018. Vol. 28, no 2, article id e1991
Keywords [en]
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
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-33516DOI: 10.1002/nem.1991Scopus ID: 2-s2.0-85029351383OAI: oai:DiVA.org:ri-33516DiVA, id: diva2:1192827
Note

Funding details: VINNOVA; Funding details: 2013-03895, VINNOVA; This research has been supported by the Swedish Governmental Agency for Innovation Systems, VINNOVA, under grant 2013-03895.

Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2018-08-17Bibliographically approved

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Gillblad, DanielStadler, Rolf

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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Language
  • de-DE
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  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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