Neuro-fuzzy processing of packet dispersion traces for highly variable cross-traffic estimation

  • Authors:
  • Marco A. Alzate;Néstor M. Peña;Miguel A. Labrador

  • Affiliations:
  • Universidad de los Andes, Bogotáia, Colombia and Universidad Distrital, Bogotáia;Universidad de los Andes, Bogotáia, Colombia;University of South Florida, Tampa, FL

  • Venue:
  • PAM'07 Proceedings of the 8th international conference on Passive and active network measurement
  • Year:
  • 2007

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Abstract

Cross-traffic data rate over the tight link of a path can be estimated using different active probing packet dispersion techniques. Many of these techniques send large amounts of probing traffic but use just a tiny fraction of the measurements to estimate the long-run cross-traffic average. In this paper, we are interested in short-term cross-traffic estimation using bandwidth efficient techniques when the cross-traffic exhibits high variability. High variability increases the cross-correlation coefficient between cross-traffic and dispersion measurements on a wide range of utilization factors and over a long range of measurement time scales. This correlation is exploited with an appropriate statistical inference procedure based on a simple heuristically modified neuro-fuzzy estimator that achieves high accuracy, low computational cost, and very low transmission overhead. The design process led to a very simple architecture, ensuring good generalization properties. Simulation experiments show that, if the variability comes from a complex correlation structure, a single estimator can be used over a long range of utilization factors and measurement periods with no additional training.