Neuro-fuzzy Processing of Packet Dispersion Traces for Highly Variable Cross-Traffic Estimation

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.