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Learning from Network Device Statistics
RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
Federal University of Uberlândia (FACOM/UFU), Brazil.
KTH Royal Institute of Technology, Sweden.
2017 (English)In: Journal of Network and Systems Management, ISSN 1064-7570, E-ISSN 1573-7705Article in journal (Refereed) In press
Abstract [en]

We estimate end-to-end service metrics from network device statistics. Our approach is based upon statistical, supervised learning, whereby the mapping from device-level to service-level metrics is learned from observations, i.e., through monitoring the system. The approach enables end-to-end performance prediction without requiring an explicit model of the system, which is different from traditional engineering techniques that use stochastic modeling and simulation. The fact that end-to-end service metrics can be estimated from local network statistics with good accuracy in the scenarios we consider suggests that service-level properties are “encoded” in network-level statistics. We show that the set of network statistics needed for estimation can be reduced to a set of measurements along the network path between client and service backend, with little loss in estimation accuracy. The reported work is largely experimental and its results have been obtained through testbed measurements from a video streaming service and a KV store over an OpenFlow network .

Place, publisher, year, edition, pages
2017.
Keyword [en]
End-to-end performance Prediction, Feature selection, Machine learning, Network analytics, Network management, OpenFlow, Statistical learning, Feature extraction, Learning systems, Stochastic models, Stochastic systems, Video streaming, End-to-end performance, End-to-end service, Network statistics, Testbed measurements, Traditional engineerings, Video streaming services, Statistics
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:ri:diva-31335DOI: 10.1007/s10922-017-9426-zScopus ID: 2-s2.0-85029795404OAI: oai:DiVA.org:ri-31335DiVA: diva2:1147605
Available from: 2017-10-06 Created: 2017-10-06 Last updated: 2017-10-06Bibliographically approved

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Citation style
  • apa
  • harvard1
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  • vancouver
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More styles
Language
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  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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