Modeling of Mathematical Processing of Physics Experimental Data in the Form of a Non-Markovian Multi-Resource Queuing System

Lisovskaya, Ekaterina; Moiseev, Alexander; Moiseeva, Svetlana; Pagano, Michele

doi:10.1007/s11182-019-01655-6

Cited by 10 publications

(2 citation statements)

References 3 publications

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“…In particular, these can be queuing systems (QS) that are well suited for describing objects where the same type of actions are regularly repeated. Queuing systems are most often used to simulate the operation of information [7,8] and telecommunication systems [9][10][11]. The features of such systems are the random nature of the data receipt and non-deterministic processing, regardless of their semantic meaning.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Railway Marshalling Yards Based on Four-Phase Queuing Systems

Zharkov

Lempert

Pavidis

2021

Information Technologies and Mathematical Modelling. Queueing Theory and Applications

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Design and study of mathematical models of marshalling yards in order to increase productivity and ensure their smooth operation is relevant, since these objects are key elements for the organization of freight transport on the railway network. In this work, we develop a mathematical model for the operation of a marshalling yard in the form of a four-phase queuing system with BMAP flow and group service of requests. Each phase is a non-Markov multichannel queuing system with a finite queue and group service of requests in the channel. For its numerical study, we create and implement a simulation model. The proposed mathematical apparatus and software are tested on for the operating marshalling yard, which is typical and located on the East Siberian Railway. We demonstrate that it allows us to assess the current level of operation, determine the maximum permissible load and find bottlenecks in the structure of the selected station and then eliminate them. Keywords: Mathematical modeling • Simulation model • Multiphase queuing system • BMAP • Railway marshalling yard The reported study was funded by RFBR, project number 20-010-00724, and by RFBR and the Government of the Irkutsk Region, project number 20-47-383002.

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Section: Introductionmentioning

confidence: 99%

Simulation of Railway Marshalling Yards Based on Four-Phase Queuing Systems

Zharkov

Lempert

Pavidis

2021

Information Technologies and Mathematical Modelling. Queueing Theory and Applications

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“…When queuing theory is applied to modeling modern information technology systems, one should take into account various system's features, such as the reliability of a radio channel in wireless communication networks. The latest models from references [1,2] are widely used for the analysis of such networks. In these models, the analytical solution is often cumbersome or difficult to solve and there is a need for approximate [3] or special [4] computational algorithms.…”

Section: Introductionmentioning

confidence: 99%

Computing the Stationary Distribution of Queueing Systems with Random Resource Requirements via Fast Fourier Transform

Naumov¹,

Gaidamaka

Samouylov

2020

Mathematics

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Queueing systems with random resource requirements, in which an arriving customer, in addition to a server, demands a random amount of resources from a shared resource pool, have proved useful to analyze wireless communication networks. The stationary distributions of such queuing systems are expressed in terms of truncated convolution powers of the cumulative distribution function of the resource requirements. Discretization of the cumulative distribution function and the application of the fast Fourier transform are a traditional way of calculating convolutions. We suggest finding truncated convolution powers of the cumulative distribution functions by calculating the convolution powers of the truncated cumulative distribution functions via fast Fourier transform. This radically decreases computational complexity. We introduce the concept of resource load and investigate the accuracy of the proposed method at low and high resource loads. It is shown that the proposed method makes it possible to quickly and accurately calculate truncated convolution powers required for the analysis of queuing systems with random resource requirements.

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