Version 1
: Received: 5 March 2021 / Approved: 10 March 2021 / Online: 10 March 2021 (10:33:30 CET)
How to cite:
Millán, G. Traffic Flows Analysis in High-Speed Computer Networks Using Time Series. Preprints2021, 2021030275. https://doi.org/10.20944/preprints202103.0275.v1
Millán, G. Traffic Flows Analysis in High-Speed Computer Networks Using Time Series. Preprints 2021, 2021030275. https://doi.org/10.20944/preprints202103.0275.v1
Millán, G. Traffic Flows Analysis in High-Speed Computer Networks Using Time Series. Preprints2021, 2021030275. https://doi.org/10.20944/preprints202103.0275.v1
APA Style
Millán, G. (2021). Traffic Flows Analysis in High-Speed Computer Networks Using Time Series. Preprints. https://doi.org/10.20944/preprints202103.0275.v1
Chicago/Turabian Style
Millán, G. 2021 "Traffic Flows Analysis in High-Speed Computer Networks Using Time Series" Preprints. https://doi.org/10.20944/preprints202103.0275.v1
Abstract
This article explores the required amount of time series points from a high-speed traffic network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series, followed by addressing the minimum amount of points required to obtain accurate estimates of the Hurst exponent in real-time. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behavior, standard deviation, mean square error, and convergence using fractional Gaussian noise signals with stationary increases. Our results show that the Whittle estimator successfully estimates the Hurst exponent in series with few points. Based on the results obtained, a minimum length for the time series is empirically proposed. Finally, to validate the results, the methodology is applied to real traffic captures in a high-speed network based on the IEEE 802.3ab standard.
Keywords
Computer networks, Hurst exponent, IEEE 802.3ab standard, Time series analysis, Traffic flows analysis.
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
