Open AccessApproval of the method of modeling of regenerating systems by the example of modeling the process of functioning of the information system taking into account service of various types of applications
Author(s) -
M. V. Zamoryonov,
V. Ya. Kopp,
D. V. Zamoryonova
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1582/1/012090
Subject(s) - markov chain , phase type distribution , process (computing) , markov process , distribution (mathematics) , computer science , state space , stationary distribution , mathematical optimization , random variable , phase (matter) , algorithm , work (physics) , space (punctuation) , phase space , function (biology) , mathematics , statistics , mathematical analysis , machine learning , biology , mechanical engineering , chemistry , physics , organic chemistry , evolutionary biology , engineering , thermodynamics , operating system
The article verifies the method of modeling regenerative systems with a common phase state space using the example of phase enlargement of the model of the information system functioning process taking into account the servicing of various types of applications. The distribution functions of random variables obtained using the method proposed in this work, based on the theorem on the distribution functions of the residence time in a subset of continuous states, and obtained using the classical phase enlargement algorithm, are compared, and the transition probabilities of the enlarged system and the stationary distribution of the embedded circuit are determined and compared Markov. It shows the coincidence of the results of phase enlargement carried out by the method proposed in the work and the classical algorithm of phase enlargement. This work clearly demonstrates the simplicity and convenience of the proposed method of phase enlargement due to the fact that there is no need to determine the stationary distribution of the embedded Markov chain for systems with a common phase state space, which is a rather complicated task that requires solving systems of integral equations containing sums and differences of variables. Currently, a general solution to this problem is unknown, although there are solutions for individual cases.