DEVELOPMENT OF A METHOD FOR ASSESSING THE ADEQUACY OF A COMPUTER SYSTEM MODEL BASED ON PETRI NETS
Main Article Content
Abstract
Topicality. The purpose of modeling any system using a Petri net is to study the behavior of the modeled system based on the analysis of the defined properties of the Petri net. Therefore, it is necessary to develop a method for assessing the adequacy of the model, based on the assessment of the degree of its correspondence to the behavior of the system. The object of research is the behavior of a system model built using a Petri net. The subject of the research is the value of the deviation of the simulated processes from the real values. The goal of the research is to develop a method for assessing the adequacy of the description of the dynamics of the researched process in a model of a computer system based on Petri nets. Results obtained. A mathematical model is built, which is determined by the number of system states. In the model, options for the analysis of the state trace of the system are considered. Analysis of the adequacy of the synthesized model and its refinement are carried out according to the developed iterative algorithm. The option of testing the hypothesis regarding the Markov nature of the processes of changing system states is considered in detail. For this, appropriate statistical criteria are proposed. Considered example of evaluation of a given path of states. To test the proposed method, a study of the management algorithm of the metropolis's transport system was conducted. The simulation results practically coincided with the real results. Conclusions. The developed method makes it possible to assess the adequacy of the model based on Petri nets with accuracy to the entered assumptions. The method allows timely background history of dynamic processes and justify the choice of its length. The method also allows reducing the possibility of an irrational increase in the size of the synthesized model.
Article Details
References
Dorrer, M.G., Popov, A.A. (2020), “Application of Process Mining technology to analyze the correspondence of life cycles of information resources to a generalized model”, Journal of Physics: Conference Series, vol. 1679,is. 3, number 032058, doi: https://doi.org/10.1088/1742-6596/1679/3/032058
Amarasinghe, P.A.G.M., Abeygunawardane, S.K. (2023), “Application of Metaheuristic Algorithms for Generation System Adequacy Evaluation”, Moratuwa Engineering Research Conference, MERCon, pp. 246–251, doi: https://doi.org/10.1109/MERCon60487.2023.10355460
Gadetska, S., Dubnitskiy, V., Kushneruk, Y., Ponochovnyi, Y. and Khodyrev, A. (2024), “Simulation of exchange processes in multi-component environments with account of data uncertainty”, Advanced Information Systems, vol. 8, no. 1, pp. 12–23, doi: https://doi.org/10.20998/2522-9052.2024.1.02
Kuchuk, N., Mozhaiev, O., Semenov, S., Haichenko, A., Kuchuk, H., Tiulieniev, S., Mozhaiev, M., Davydov, V., Brusakova, O. and Gnusov, Y. (2023), “Devising a method for balancing the load on a territorially distributed foggy environment”, Eastern-European Journal of Enterprise Technologies, vol. 1(4 (121), pp. 48–55, doi: https://doi.org/10.15587/1729-4061.2023.274177
Kuchuk, H., Kovalenko, A., Ibrahim, B.F. and Ruban, I. (2019), “Adaptive compression method for video information”, International Journal of Advanced Trends in Computer Science and Engineering, vol. 8(1), pp. 66–69, doi: http://dx.doi.org/10.30534/ijatcse/2019/1181.22019
Kalinin, Y., Kozhushko, A., Rebrov, O. and Zakovorotniy, A. (2022), “Characteristics of Rational Classifications in Game-Theoretic Algorithms of Pattern Recognition for Unmanned Vehicles”, 2022 IEEE 3rd KhPI Week on Advanced Technology (KhPIWeek), Kharkiv, Ukraine, 2022, pp. 1–5, doi: http://dx.doi.org/10.1109/KhPIWeek57572.2022.9916454
Dotsenko, N., Chumachenko, I., Galkin, A., Kuchuk, H. and Chumachenko, D. (2023), “Modeling the Transformation of Configuration Management Processes in a Multi-Project Environment”, Sustainability (Switzerland), vol. 15(19), 14308, doi: https://doi.org/10.3390/su151914308
Suvorov, N.M. and Lomazova, I.A. (2024), “Verification of data-aware process models: Checking soundness of data Petri nets”, Journal of Logical and Algebraic Methods in Programming, vol. 138, doi: https://doi.org/10.1016/j.jlamp.2024.100953
Castillo, J.A.R. and Malinao, J.A. (2024), “Model Decomposition of Robustness Diagram with Loop and Time Controls to Petri Net with Considerations on Resets”, Lecture Notes in Computer Science, vol. 14799 LNCS, pp.193–201, doi: https://doi.org/10.1007/978-3-031-63031-6_17
Capra, L. and Köhler-Bußmeier, M. (2024), “Modular rewritable Petri nets: An efficient model for dynamic distributed systems”, Theoretical Computer Science, vol. 990, 114397, doi: https://doi.org/10.1016/j.tcs.2024.114397
Petrovska, I., Kuchuk, H., Kuchuk, N., Mozhaiev, O., Pochebut, M. and Onishchenko, Yu. (2023), “Sequential Series-Based Prediction Model in Adaptive Cloud Resource Allocation for Data Processing and Security”, 2023 13th International Conference on Dependable Systems, Services and Technologies (DESSERT 2023), 13–15 October, Athens, Greece, code 197136, doi: https://doi.org/10.1109/DESSERT61349.2023.10416496
Zhang, M., Alfieri, A. and Matta, A. (2024), “Generation of mathematical programming representations for discrete event simulation models of timed petri nets”, Discrete Event Dynamic Systems: Theory and Applications, vol. 34, is, 1, pp. 1–19, doi: https://doi.org/10.1007/s10626-023-00387-7
Shyshatskyi, A., Stasiuk, T., Odarushchenko, E., Berezanska, K. and Demianenko, H. (2023), “Method of assessing the state of hierarchical objects based on bio-inspired algorithms”, Advanced Information Systems, vol. 7, no. 3, pp. 44–48, doi: https://doi.org/10.20998/2522-9052.2023.3.06
Taghinezhad-Niar, A. (2024), “A Client-Centric Consistency Model for Distributed Data Stores using Colored Petri Nets”, 2024 10th Int. Conf. on Web Research, ICWR 2024, pp. 309–314, doi: https://doi.org/10.1109/ICWR61162.2024.10533365
Kuchuk, G.A., Akimova, Yu.A. and Klimenko, L.A. (2000), “Method of optimal allocation of relational tables”, Engineering Simulation, vol. 17, is. 5, pp. 681–689, available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-0034512103&origin=resultslist
Yang, Y., Liu, X. and Lu, W. (2023), “A Cyber–Physical Systems-Based Double-Layer Mapping Petri Net Model for Factory Process Flow Control”, Applied Sciences (Switzerland), vol. 13, is. 15, 8975, doi: https://doi.org/10.3390/app13158975
Castellanos Contreras, J.U. and Rodríguez Urrego, L. (2023), “Technological Developments in Control Models Using Petri Nets for Smart Grids: A Review”, Energies, vol. 16, is. 8, 3541, doi: https://doi.org/10.3390/en16083541
Petrovska, I., Kuchuk, H. and Mozhaiev, M. (2022), “Features of the distribution of computing resources in cloud systems”, 2022 IEEE 4th KhPI Week on Advanced Technology, KhPI Week 2022 – Conference Proceedings, 03-07 October 2022, Code 183771, doi: https://doi.org/10.1109/KhPIWeek57572.2022.9916459
He, L., Liu, G. and Zhou, M. (2023), “Petri-Net-Based Model Checking for Privacy-Critical Multiagent Systems”, IEEE Transactions on Computational Social Systems, vol. 10, is. 2, pp. 563–576, doi: https://doi.org/10.1109/TCSS.2022.3164052
Semenov, S., Sira, O. and Kuchuk, N. (2018), “Development of graphicanalytical models for the software security testing algorithm”, Eastern-European Journal of Enterprise Technologies, vol. 2, no. 4 (92), pp. 39–46, doi: https://doi.org/10.15587/1729-4061.2018.127210
Yarava, A. and Bindu, C.S. (2023), “An efficient trust inference model in online social networks using fuzzy petri nets”, Concurrency and Computation: Practice and Experience, vol. 35, is. 6, doi: https://doi.org/10.1002/cpe.7583
Serrano, S. and Scarpa, M. (2023), “A Petri Net Model for Cognitive Radio Internet of Things Networks Exploiting GSM Bands”, Future Internet, vol. 15, is. 3, number115, doi: https://doi.org/10.3390/fi15030115
Kuchuk, H. and Malokhvii, E. (2024), “Integration of IOT with Cloud, Fog, and Edge Computing: A Review”, Advanced Information Systems, vol. 8, no. 2, pp. 65–78, doi: https://doi.org/10.20998/2522-9052.2024.2.08
Wiśniewski, R., Patalas-Maliszewska, J., Wojnakowski, M., Topczak, M. and Zhou, M. (2023), “Fast Verification of Petri Net-Based Model of Industrial Decision-Making Systems: A Case Study”, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, pp. 3316–3322, doi: https://doi.org/10.1109/SMC53992.2023.10394156
Petrovska, I. and Kuchuk, H. (2023), “Adaptive resource allocation method for data processing and security in cloud environment”, Advanced Information Systems, vol. 7(3), pp. 67–73, doi: https://doi.org/10.20998/2522-9052.2023.3.10
Jiang, W., Zhou, K.-Q., Sarkheyli-Hägele, A. and Zain, A.M. (2022), “Modeling, reasoning, and application of fuzzy Petri net model: a survey”, Artificial Intelligence Review, vol. 55(8), pp. 6567–6605, doi: https://doi.org/10.1007/s10462-022-10161-0
Merlac, V., Smatkov, S., Kuchuk, N. and Nechausov, A. (2018), “Resourses Distribution Method of University e-learning on the Hypercovergent platform”, Сonf. Proc. of 2018 IEEE 9th Int. Conf. on Dependable Systems, Service and Technologies. DESSERT’2018, Kyiv, May 24-27, 2018, pp. 136–140, doi: http://dx.doi.org/10.1109/DESSERT.2018.8409114
Geronimo, M. F., Martinez, E. G. H., Vazquez, E. D. F., Godoy, J. J. F. and Anaya, G. F. (2021), “A multiagent systems with Petri Net approach for simulation of urban traffic networks”, Computers, Environment and Urban Systems, vol. 89, 101662, doi: https://doi.org/10.1016/j.compenvurbsys.2021.101662
Li, J., Wang, Z., Sun, L. and Wang W. (2021), “Modeling and Analysis of Network Control System Based on Hierarchical Coloured Petri Net and Markov Chain”, Discrete Dynamics in Nature and Society (DDNS), Article ID 9948855, doi: https://doi.org/10.1155/2021/9948855
Borodin, P.A. and Ershov, A.M. (2024), “S. R. Nasyrov’s Problem of Approximation by Simple Partial Fractions on an Interval”, Mathematical Notes, vol. 115, no. 3-4, pp. 520–527, doi: https://doi.org/10.1134/S0001434624030234
Chang, S., Li, D. and Qi, Y. (2023), “Pearson's goodness-of-fit tests for sparse distributions”, Journal of Applied Statistics, vol. 50, is. 5, pp. 1078–1093, doi: https://doi.org/10.1080/02664763.2021.2017413