Modeling event flows at the input of information systems with a message broker

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Abstract

The problem of modeling event flows at the input of information systems using a message broker is considered. Approaches are proposed that allow reproducing key statistical characteristics of real event flows, such as intensity, temporal structure, and seasonal fluctuations, based on both historical and real-time data. Two modeling algorithms are described: an adaptive algorithm for modeling the flow of events with a limited aftereffect, based on the Lainiotis separation method, and an algorithm based on the Markov model for changing discrete states that determine the flow parameters. Experiments have been conducted to compare the flows of events operating in a real system with a message broker and flows modeled using the proposed algorithms.

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About the authors

P. A. Parinov

Voronezh State University

Author for correspondence.
Email: parinov_p@sc.vsu.ru

Assistant of the Department

Russian Federation, Voronezh

A. A. Sirota

Voronezh State University

Email: sir@cs.vsu.ru

Dr. of Tech. Sc., Head of the Department

Russian Federation, Voronezh

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Supplementary files

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2. Fig. 1. General algorithm for generator synthesis

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3. Fig. 2. Evolution of the posterior probabilities of hypotheses as observations of event arrival times are received

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4. Fig. 3. Graph for three states

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5. Fig. 4. Timing diagram of the Markov chain

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6. Fig. 5. Comparison of real and synthetic event streams for the adaptive model

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7. Fig. 6. Comparison of real and synthetic event streams for the MPSS model

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8. Fig. 7. Comparison of real and synthetic event streams for the LSTM model

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