Abstract
The present article deals with statistical university network traffic, by applying the methods of self-similarity and chaos analysis. The object of measurement is Šiauliai University LitNet network node maintaining institutions of education of the northern Lithuania region. Time series of network traffic characteristics are formed by registering amount of information packets in a node at different regimes of network traffic and different values of discretion of registered information are present. Measurement results are processed by calculating Hurst index and estimating reliability of analysis results by applying the statistical method. Investigation of the network traffic allowed us drawing conclusions that time series bear features of self-similarity when aggregated time series bear features of slowly decreasing dependence.
Highlights
Empirical research of computer network packet traffic shows that it is attributed with self-similarity [1, 2, 6, 8]
Analyse network traffic as a fractal process attributed with a second order statistical self-similarity which is characterised by a fractal measure [6]
If Hurst coefficient H = 0.5, it means that sequence members are random and its every subsequent member does not depend on previous series members; in an opposite case, we can state that previous events recorded in time series have constant influence on further processes and this influence is the stronger the closer the event is to the past
Summary
Empirical research of computer network packet traffic shows that it is attributed with self-similarity [1, 2, 6, 8] After estimating the latter feature, it is possible to adequately prognosticate the change of traffic and to apply the prognosis results in increase of network throughput and improvement of its QoS quality of service, while regulating packet latency, fluctuation restriction and packet loss transportation on data and physical OSI layers [3, 10]. J. Beran, analyse network traffic as a fractal process attributed with a second order statistical self-similarity which is characterised by a fractal measure [6]. Measurement results were processed by estimating the fractal measure and calculating Hurst coefficient and statistically estimating reliability of analysis results [6]
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