The properties of the queuing model with the parallel structure

Chechelnitsky, O. A.

doi:10.17721/1812-5409.2020/4.11

The properties of the queuing model with the parallel structure

Тип публікації :

Стаття

Дата випуску :

2020

Автор(и) :

Chechelnitsky, O. A.

Мова основного тексту :

Англійська

eKNUTSHIR URL :

https://ir.library.knu.ua/handle/15071834/26183

DOI :

10.17721/1812-5409.2020/4.11

Журнал :

Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics

Випуск :

4

ISSN :

1812-5409

Початкова сторінка :

79

Кінцева сторінка :

82

Цитування :

[APA 7] Chechelnitsky, O. A. (2020). The properties of the queuing model with the parallel structure. Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics, (4), 79–82. https://doi.org/10.17721/1812-5409.2020/4.11

[ДСТУ] Chechelnitsky O. A. The properties of the queuing model with the parallel structure. Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics. 2020. no. 4. P. 79—82. DOI: 10.17721/1812-5409.2020/4.11 (date of access: 18.07.2026).

The present article is devoted to research the multi-channelk model with the parallel structure. It means that we consider the model which consists of two infinite-server queues.The service time in the each system has general function of distribution. In this case the stochastic dynamic of our model cannot be defined by Markov chain. As a result, analysis of such models is much more difficult than that of the corresponding Markovian queueing models. Besides we assume that customers arrive to our model according a bivariate Poisson input flow. This input process is characterized by the fact that customers arrive according to a bivariate Poisson flow simultaneously. We consider the number of customers in the systems at time t. This stochastic process describes the state of our model. In present paper we find the limit joint distribution of the number of customers in the systems. In a general way (by differentiating the corresponding generating function.) we obtain the main characteristics of this distribution, such as the expected number of customers in the nodes, its variance and correlation. In the case when parameters of our model dependent on the parameter n (number of series) the limit normal distribution was obtained for the service process in the stationary regime.Key words: network model, bivariate input flow, stationary distribution.Pages of the article in the issue: 79 - 82Language of the article: Ukrainian

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