Recursive Selection of Hyperexponential Distributions in Approximation of Distributions with "Heavy Tails"
- Authors: Buranova M.A.1, Kartashevskiy V.G.1
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Affiliations:
- Povolzhskiy State University of Telecommunications and Informatics
- Issue: Vol 9, No 2 (2023)
- Pages: 40-46
- Section: Articles
- URL: https://journals.rcsi.science/1813-324X/article/view/254363
- DOI: https://doi.org/10.31854/1813-324X-2023-9-2-40-46
- ID: 254363
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Abstract
It is known that many quantities that determine the network characteristics of the functioning of an infocommunication network have probability distributions with "heavy tails", which can have a significant impact on network performance. Models with heavy-tailed distributions tend to be difficult to analyze. The analysis can be simplified by using an algorithm to approximate a heavy-tailed distri-bution by a hyperexponential distribution (a finite mixture of exponentials). The paper presents a algorithm for calculating the parameters of the hyperexponential distribution components, which is based on a recursive selection of parameters. This algorithm allows you to analyze various models of queues, including G/G/1. It is shown that the approach under consideration is applicable to the approxi-mation of monotonically decreasing distributions, including those with a "heavy tail". Examples of approximation of Pareto and Weibull distributions are given.
About the authors
M. A. Buranova
Povolzhskiy State University of Telecommunications and Informatics
Email: m.buranova@psuti.ru
ORCID iD: 0000-0003-2986-8252
V. G. Kartashevskiy
Povolzhskiy State University of Telecommunications and Informatics
Email: v.kartashevskiy@psuti.ru
ORCID iD: 0000-0003-1114-3966
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