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This article explores the required amount of time series points from ahigh-speed traffic network to accurately estimate the Hurst exponent. Themethodology consists in designing an experiment using estimators that areapplied to time series, followed by addressing the minimum amount of pointsrequired to obtain accurate estimates of the Hurst exponent in real-time. Themethodology addresses the exhaustive analysis of the Hurst exponent consideringbias behavior, standard deviation, mean square error, and convergence usingfractional gaussian noise signals with stationary increases. Our results showthat the Whittle estimator successfully estimates the Hurst exponent in serieswith few points. Based on the results obtained, a minimum length for the timeseries is empirically proposed. Finally, to validate the results, themethodology is applied to real traffic captures in a high-speed network basedon the IEEE 802.3ab standard.

Traffic Flows Analysis in High-Speed Computer Networks Using Time Series