Modeling Link Events in High Reliability Networks With Support Vector Machines Articles uri icon

authors

  • FEIJOO MARTINEZ, JUAN RAMON
  • ROJO ALVAREZ, JOSE LUIS
  • CID SUEIRO, JESUS
  • CONDE PARDO, PATRICIA
  • MATA-VIGIL ESCALERA, J. L.

publication date

  • March 2010

start page

  • 191

end page

  • 202

issue

  • 1

volume

  • 59

international standard serial number (ISSN)

  • 0018-9529

electronic international standard serial number (EISSN)

  • 1558-1721

abstract

  • High reliability communication networks (HRCN) are characterized by very low failure rates, and extremely short unavailability periods. The accurate modeling of the link availability in HRCN is a non-trivial
    problem, given that an extremely low number of historic events have been
    observed. We propose a statistical learning model for link event
    prediction in HRCN based on support vector machines (SVM) for nonlinear
    regression. The model flexibility can be improved by grouping predictor
    variables of different nature. A surrogate data set is made, which
    mimics the basic properties of links in a real network, and it is used
    for simulations that yield basic knowledge about the use and performance
    of the proposed SVM model. A true network example, based on two years
    of historic data, is also analysed. The proposed SVM model yields better
    performance than other tested methods (frequentist, and neural network
    estimators), specially during the first years obtaining the historic
    data in a HRCN, when the number of events is critically low.