To study the effective functioning and behavior of parallel computing systems (which may be an integral part of the Cyber-Physical System), a high-performance software package based on mat hematical models, methods and algorithms for stochastic modeling has been developed at the design stage. This software package completely solves the design problem — the parameters of a computi ng system have been calculated: its computational power, the average value of task executiontime or various tasks on homogeneous resources of a parallel computing system, the distribution function of the task execution time. Based on the analysis of the parameters obtained, as well as indicators of the reliability of the system, the configuration of a parallel computing system has been selected or the possibility of using a previously selected computing systemtoperform the task has been considered.
 Tsvetkov V. Ya., Alpatov A. N. Problems of distributed systems // Prospects of science and education — 2014. — No. 6. — P. 31–36.
 Khaitan et al., «Design Techniques and Applications of Cyber Physical Systems: A Survey», IEEE Systems Journal, 2014.
 Rad, Ciprian-Radu; Hancu, Olimpiu; Takacs, Ioana-Alexandra; Olteanu, Gheorghe (2015). «Smart Monitoring of Potato Crop: A Cyber-Physical System Architecture Model in the Field of Precision Agriculture». Conference Agriculture for Life, Life for Agriculture. 6: 73–79.
 Bocharov P. L., Ignatushchenko V. V. Mathematical models and methods for evaluating the effectiveness of parallel computing systems on complexes of interrelated jobs // Tez. report international conf, «High-Performance Computing Systems in Management and Scientific Research,» Alma-Ata, 1991, p. 6.
 Ignatushchenko V. V., Klushin Y. S. Prediction of the implementation of complex software systems on parallel computers: direct stochastic modeling // Automation and Remote Control. 1994. No. 12, p. 142–157.
 Khritankov A. S. Mathematical model of performance characteristics of distributed computing systems. Computer science, management, economics. JOBS OF MIPT. — 2010. — Vol. 2, No. 1 (5), p. 110–115.
 Ivutin A. N., Larkin E. V. Prediction of the execution time of the algorithm. Magazine. News of TSU. Technical science. Issue number 3/2013 C 301–315.
 Ivanov N. N. Mathematical prediction of reliable execution of sets of tasks with symmetric runtime distributions. Journal of Open Education, Issue No. 2–2 / 2011, p. 52–55.
 Kulagin V. P., Problems of parallel computing systems Perspectives of Science & Education. 2016. 1 (19) International Scientific Electronic Journal ISSN 2307–2334 (Online)
 Bondur V. G. Modern approaches to the processing of large flows of hyperspectral and multispectral aerospace information // Study of the Earth of their cosmos. 2014. No. 1. P. 4–17
 Salibekyan S. M., Panfilov P. B. Questions of automaton-network modeling of computer systems with data flow control // Information technologies and computer systems. 2015. No. 1. P. 3–9.
 Kulikov, I., Chernykh, I., Glinsky, B., Weins, D., Shmelev, A. Astrophysics simulation on RSC massively parallel architecture // Proc. 2015 IEEE/ACM 15th Int. Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015. IEEE Press, 2015.1131–1134.
 Boccara N. Modeling Complex Systems. NY: Springer, 2004. 397 p. Lublinsky B. Defining SOA as an architectural style. 9 January 2007. [Electronic resource]
 Ivanov S.V., Identification of Parametrically Connected Models of Complex Systems, Nauch.-tekhnich. we know SPSU ITMO. High-performance computing and computer modeling technologies. 2008. Vol. 54. pp. 100–107.
 Ivanov N. N., Ignatushchenko V. V., Mikhailov A. Y., Static prediction of the execution time of complexes of interrelated jobs in multiprocessor computing systems, Avtomat. and Telemekh., 2005, issue 6, 89–103.
 Ignatushchenko V. V., Klushin Y. S. Prediction of the implementation of complex software systems on parallel computers: direct stochastic modeling // Automation and Remote Control. 1994. N12, p. 142–157.
 Klushin, Y. S. Improving the accuracy of estimating the execution time of folding software systems in multiprocessor computer systems for belt stochastic modeling. Bulletin of NU «Lviv Polytechnic» No. 881. Computer systems and netjobs. — Lviv: NU «LP», 2017.
 Klushin Y. S. reducing the number of states of the Markov process when executing complex software systems on parallel computers. Scientific Bulletin of Chernivtsi University. Computer systems and components. 2016. T. 7. Vol. 2, pp. 53–62.
 Reibman A. L., Trivedi K. S. Numerical transient analysis of Markov models // Computers and Operations Research. 1988. Vol. 15. No. 1. P. 19–36.
 Preidunov Y. V. Development of mathematical models and methods for predicting the implementation of complex software systems on parallel computing systems. PhD thesis. M.: Inst. Of Problems of Management RAS, 1992.