Keywords: hidden Markov model, queuing system, GI/G/2/0 with losses, merged semi-Markov model, state forecasting
Hidden Markov model of a GI/G/2/0 queuing system with losses
UDC 519.872.1; 519.217
DOI: 10.26102/2310-6018/2022.39.4.012
Semi-Markov processes are widely used to model queuing systems. The relevance of the study is due to the increase in the capabilities for analysis and performance of queuing systems for which semi-Markov models are constructed. The application of the hidden Markov model theory to them also underscores the importance of this research. In this regard, this article discusses the application of the apparatus of the hidden Markov models theory to a lossy queuing system described by a semi-Markov process with a general phase state space. This makes it possible not only to move beyond the exponential law of the distribution of service times and the flow of applications when describing the system, but also to solve the problems of forecasting and evaluating states and signals, correcting the model while the system is in operation. For transition to a discrete set of states of the Semi-Markov model, the algorithm of stationary phase enlargement is employed. As an illustrative example, a merged semi-Markov model of the GI/G/2/0 queuing system with losses is constructed. Based on it, a hidden Markov model is developed for which the problems of analyzing dynamics and predicting states are solved. The parameters of the hidden Markov model are refined by means of the Baum-Welsh algorithm; the most probable sequence of changing states of the system is determined by the received signal vector.
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Keywords: hidden Markov model, queuing system, GI/G/2/0 with losses, merged semi-Markov model, state forecasting
For citation: Sidorov S.М., Obzherin Y.E. Hidden Markov model of a GI/G/2/0 queuing system with losses. Modeling, Optimization and Information Technology. 2022;10(4). URL: https://moitvivt.ru/ru/journal/pdf?id=1256 DOI: 10.26102/2310-6018/2022.39.4.012 (In Russ).
Received 26.10.2022
Revised 06.12.2022
Accepted 19.12.2022
Published 31.12.2022