|

Classification of modeling methods for distributed information processing systems

Authors: Adamova I.O.
Published in issue: #2(55)/2021
DOI: 10.18698/2541-8009-2021-2-676


Category: Informatics, Computer Engineering and Control | Chapter: System Analysis, Control, and Information Processing

Keywords: distributed systems, modeling of distributed systems, finite automata, probabilistic automata, Petri nets, nested Petri nets, process algebras, aggregate system, agent-based model, graph
Published: 22.03.2021

A large number of different formalisms and methods are used to simulate distributed information processing systems, but not each of them can sufficiently reflect all the properties of such complex systems. The following formalisms are considered as analysis and control tools: process algebras, aggregate systems, agent-based models, as well as such types of graphs as finite automata, probabilistic automata, Petri nets, and queuing systems. A classification of the listed tools is given, their main features are noted, options for using existing software for working with them are proposed, the most suitable formalism is highlighted. Based on these features, it was concluded that nested multilevel Petri nets are the most suitable for the analysis and control of complex systems.


References

[1] Gabalin A.V. Optimization issues of distributed data processing systems structure. Prikladnaya informatika [Journal of Applied Informatics], 2007, no. 6, pp. 129–139 (in Russ.).

[2] Gurin R.E., Rudakov I.V., Rebrikov A.V. Methods of software verification. Nauka i obrazovanie: nauchnoe izdanie [Science and Education: Scientific Publication], 2015, no. 10. URL: http://engineering-science.ru/doc/823129.html (in Russ.).

[3] TismTool: website. URL: www.tismtool.com (accessed: 11.12.2019).

[4] Staroletov S.M., Kryuchkova E.N. Modelirovanie raspredelennykh mnogokomponentnykh programmnykh sistem i ikh testirovanie na osnove avtomatnykh veroyatnostnykh modeley [Modelling of distributed multicomponent software systems and their testing based on automated probability models]. Barnaul, Izd-vo AltGTU Publ., 2011 (in Russ.).

[5] Kotov V.E. Seti Petri [Petri networks]. Moscow, Nauka Publ., 1984 (in Russ.).

[6] Lomazova I.A. Vlozhennye seti Petri [Nested Petri network]. Moscow, Nauchnyi mir Publ., 2004 (in Russ.).

[7] Romeo: website. URL: http://romeo.rts-software.org (accessed: 11.12.2019).

[8] CPNTools: website. URL: http://cpntools.org (accessed: 11.12.2019).

[9] Dvoryanskiy L.V., Lomazova I.A. CPN tools-assisted simulation and verification of nested Petri nets. Modelirovanie i analiz informatsionnykh sistem, 2012, vol. 19, no. 5, pp. 115–130 (in Russ.). (Eng. version: Aut. Control Comp. Sci., 2013, vol. 47, no. 7, pp. 393–402. DOI: https://doi.org/10.3103/S0146411613070201)

[10] Fokkink W. Introduction to process algebra. Springer, 2007.

[11] Lychkina N.N. Imitatsionnoe modelirovanie ekonomicheskikh protsessov [Imitation modeling of economic processes]. Moscow Akademiya AyTi Publ., 2005 (in Russ.).

[12] Lebedyuk E.A. Agent-based modelling: state and prospects. Vestnik REA im. G.V. Plekhanova [Vestnik of the Plekhanov Russian University of Economics], 2017, no. 6, pp. 155–162 (in Russ.).

[13] NetLogo: website. URL: https://ccl.northwestern.edu/netlogo (accessed: 11.12.2019).