Stochastic petri nets software tools

Автор: Bystrov A.V., Virbitskaite I.B., Oshevskaya E.S.

Журнал: Проблемы информатики @problem-info

Рубрика: Прикладные информационные технологии

Статья в выпуске: 2 (63), 2024 года.

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The behavior of a wide variety of systems, biochemical, transport, industrial, software, and so on, is inherently parallel, non-deterministic, and stochastic. The study and design of these systems requires the use of models that take into account all these aspects, as well as appropriate software tools. Stochastic Petri nets and their various extensions are successfully used as such models. They combine the clarity and intuitiveness of the graphical representation with well-developed mathematical and algorithmic apparatus of analysis. These models allow us to study not only qualitative but also quantitative properties of systems, such as bandwidth, reliability, waiting time, etc. Software tools that support the construction, modification and analysis of system models based on various variants of stochastic Petri nets have already been developed and continue to appear. This paper provides a detailed overview of several such multiplatfom software tools, namely, GreatSPN, ORIS, PetriNuts, TimeNet and PIPE2 that are available on the Internet, and got recognized by users. The introduction, informally, but with proper references to the literature, gives the basic concepts, defines the classes of Petri nets and terms used later. Then, for each of the software tools, its structure, features and peculiarities are considered. The tools are then compared in terms of their functional and performance analysis capabilities, and recommendations to users on how to use the tools depending on what type of stochastic models need to be investigated are discussed. The main purpose of the paper is to facilitate the researcher and engineer in selecting the most appropriate modeling and analysis tool for the task at hand.

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Stochastic petri nets, modelling, simulation, performance analysis, petri net tools

Короткий адрес: https://sciup.org/143183456

IDR: 143183456   |   DOI: 10.24412/2073-0667-2024-2-32-57

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