METHOD OF MATHEMATICAL MODELING OF SELF-SIMILAR TRAFFIC IN INFOCOMMUNICATION NETWORKS

Abstract

This article presents a new approach to solving the problem of adequate network traffic modeling. A novel method is proposed that allows generating self-similar packet flows with an arbitrarily specified degree of self-similarity in infocommunication networks. 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 build more realistic simulation models of infocommunication networks. The authors propose a mathematical apparatus method for the procedure of forming self-similar traffic, which consists in 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 article presents a procedure for forming self-similar traffic based on the Pareto distribution law. This law is chosen due to its ability to describe random variables with long tails, which is inherent in many real systems. The main parameter determining the level of flow selfsimilarity is the Hurst parameter, which allows flexible modeling of various traffic scenarios. The proposed procedure is an important tool for researchers and engineers engaged in modeling and analyzing network traffic, can be used to model traffic in infocommunication networks, contributing to improving the accuracy of reproducing real network operating conditions.