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Predicting real-time service-level metrics from device statistics
KTH Royal Institute of Technology, Sweden.
Ericsson Research, Sweden.
RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
Ericsson Research, Sweden.
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2015 (English)In: Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, Institute of Electrical and Electronics Engineers Inc. , 2015, p. 414-422Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2015. p. 414-422
Keywords [en]
cloud computing, machine learning, network analytics, Quality of service, statistical learning, video streaming, Artificial intelligence, Computer operating systems, Forecasting, Information services, Learning systems, Linux, Network management, Real time systems, Statistics, Cloud services, Computational loads, Performance metrics, Real time service, Server machines, Target systems, Video streaming services, Distributed computer systems
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:ri:diva-43933DOI: 10.1109/INM.2015.7140318Scopus ID: 2-s2.0-84942572120ISBN: 9783901882760 (print)OAI: oai:DiVA.org:ri-43933DiVA, id: diva2:1392953
Conference
14th IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, 11 May 2015 through 15 May 2015
Available from: 2020-02-14 Created: 2020-02-14 Last updated: 2020-02-19Bibliographically approved

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Gillblad, Daniel

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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Output format
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