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Predicting service metrics for cluster-based services using real-time analytics
KTH Royal Institute of Technology, Sweden.
Ericsson Research, Sweden.
RISE, Swedish ICT, SICS.
Ericsson Research, Sweden.
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2015 (English)In: Proceedings of the 11th International Conference on Network and Service Management, CNSM 2015, Institute of Electrical and Electronics Engineers Inc. , 2015, p. 135-143Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2015. p. 135-143
Keywords [en]
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
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:ri:diva-43902DOI: 10.1109/CNSM.2015.7367349Scopus ID: 2-s2.0-84964055190ISBN: 9783901882777 (print)OAI: oai:DiVA.org:ri-43902DiVA, id: diva2:1394359
Conference
11th International Conference on Network and Service Management, CNSM 2015, 9 November 2015 through 13 November 2015
Available from: 2020-02-18 Created: 2020-02-18 Last updated: 2020-02-19Bibliographically approved

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Gillblad, DanielStadler, Rolf

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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