Метод формування самоподібного потоку із заданим параметром Херста для моделювання трафіку в мережі
Year: 2024
Type: article
Abstract: This article addresses the problem of adequate network traffic modeling. A new method is proposed that enables the generation of self-similar packet flows with an arbitrarily specified degree of self-similarity. The method is based on the use of the Pareto distribution and the maximum likelihood method for estimating model parameters. The obtained results can be used to construct more realistic simulation models of computer networks. The authors propose a mathematical apparatus method for the procedure of forming self-similar traffic, which involves creating an accurate and efficient model that reflects the real properties of self-similarity in data flows. An effective tool for modeling complex network processes is proposed, allowing more accurate prediction of infocommunication network behavior and optimization of its operation. The proposed method can be applied to develop new data transmission protocols and analyze the efficiency of existing ones. A relationship has been obtained that allows calculating the appropriate value of the Pareto distribution parameter, which ensures the formation of a self-similar flow with the required Hurst parameter value. The procedure can be used to describe traffic when constructing a simulation model of computer network functioning.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6988243226
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