The Mathematical Model for Assessing the Reliability of Multiprocessor Computing Systems Functioning
( Pp. 22-28)

For the widespread use of information technology in the organization’s business process, which will be aimed at optimizing work and leading to increased productivity and profitability, quality software is needed. Consequently, the design and production of new software (software) requires an accurate analysis of its technical characteristics and, on this basis, will remain one of the pressing tasks in the field of information technology. Therefore, the article discusses an approach for assessing and improving the basic parameters of effective software operation. Reliability, to ensure the required performance, is the main criterion of operation, since it is the ability of a software product to reliably perform specified functions under specified conditions for the required period of time with a sufficiently high probability. The problem of software reliability deserves more and more attention due to the continuous complication of the systems being created, the increase in the range of tasks assigned to them, and, as a conclusion, a significant increase in the complexity and volume of software. New versions are used for those software modules that may experience software failures. To implement the proposed approach, a mathematical model for assessing software reliability is provided. Formulas are presented that are used to calculate the complex reliability parameters of the system under consideration. Relevant examples are shown. For this purpose, the Markov service model was used, that is, the study of queuing systems using a Markov process, which has a discrete set of states. The process of functioning of a multiprocessor computing complex consisting of identical processors is represented by a closed queuing system with waiting.

Terskov V.A., Sakash I.Yu. The Mathematical Model for Assessing the Reliability of Multiprocessor Computing Systems Functioning. Computational Nanotechnology. 2024. Vol. 11. No. 2. Pp. 22–28. (In Rus.). DOI: 10.33693/2313-223X-2024-11-2-22-28. EDN: MHZWBU

Bahnam B.S., Dawwod S.A. Younis M.C. Optimizing software reliability growth models through simulated annealing algorithm: parameters estimation and performance analysis. The Journal of Supercomputing. April 2024. DOI: 10.1007/s11227-024-06046-4
Pelliccione P., Laranjeiro N. Insights From the software reliability research community. IEEE Reliability Magazine. March 2024. DOI: 10.1109/MRL.2024.3358736
Gachaev A.M., Dataev A.A., Vazkaeva S.S.-A. Research on the reliability of computer information technology software. Applied Economic Research. 2023. No. 2. Pp. 80–84. (In Rus.)
Eze N., Ejikeme A., Guha K. RFID library management software dependability through reliable fault-detection and fault correction procedures. Microsystem Technologies. February 2024. DOI: 10.1007/s00542-023-05607-6
Wang J., Zhang C. An open-source software reliability model considering learning factors and stochastically introduced faults. Applied Sciences. January 2024. DOI: 10.3390/app14020708
Sama U., Kumar A. A software reliability model incorporating fault removal efficiency and it’s release policy. Computational Statistics. November 2023. DOI: 10.1007/s00180-023-01430-9
Qiu N. et al. Infinite-failure software reliability models based on non-homogeneous Markov Processes. Research Square. November 2023. Pp. 1–32. DOI: 10.21203/rs.3.rs-3507541/v1
Van Driel W.D. et al. Software reliability for agile testing. Mathematics. 2020. No. 8. Pp. 791–805. DOI: 10.3390/math8050791
Dobrokhvalov M.O. et al. Analysis of approaches to modeling queuing systems. News of St. Petersburg SETU “LETI”. 2021. No. 5. Pp. 56–64. (In Rus.)
Vadeiko V.S., Manko A.V. Markov reliability model. Minsk: BNTU. 2022. Pp. 222–225.
Rasulov М.М. Software reliability assessment. Current Scientific Research in the Modern World. 2020. No. 6 (62). Pp. 112–116. (In Rus.)
Orazov M.Sh., Annamuradov M.T., Vepaev Sh.V. Research on Markov service models. Young Scientist. 2022. No. 49 (444). Pp. 26–28. (In Rus.)
Kopeika E.A., Verbin A.V. A methodological approach to assessing the probability of failure-free operation of complex technical systems, taking into account the characteristics of the control system based on the Bayesian trust network. Proceedings of MAI. 2023. No. 128. (In Rus.) DOI: 10.34759/trd-2023-128-22
Sivoplyas M.A. Markov maintenance safety model for complex machinery. Reliability and Quality of Complex Systems. 2022. No. 3. Pp. 49–53. (In Rus.)

software reliability, probability, reliability model, Markov model, processor operation, Kolmogorov–Chapman system of equations.