Redundant models of testable distributed real-time computing systems

Gruzlikov A.M., Kolesov N.V. Redundant models of testable distributed real-time computing systems. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2021, vol. 21, no. 5, pp. 767–773 (in Russian). doi: 10.17586/2226-1494-2021-21-5-767-773

Diagnostic issues receive a lot of attention in the design of information processing and control systems since the systems’ reliability and fault tolerance depends on the quality of their solution. The article presents the results of the development of a synthesis algorithm for a model designed to solve the problem of test diagnostics and focused on distributed computing systems. The algorithm is integrated in the system and executed in parallel with the main software of the system, which makes it possible to simplify the process of testing the system. The description of a distributed computing system, complemented by an integrated diagnostic model, is a redundant model of the system. The proposed algorithm implies a reduced amount of diagnostic information. The diagnostic model has a hierarchical structure and involves two stages. At the first stage, the algorithm calculates the set of paths that make up the coverage of its edges for the graph of intermodular connections in the system. It matches a chain of dynamic links with each of the obtained paths, the number of the links being equal to the number of software modules through which this path passes. At the second stage, the type of dynamic links is determined. It is taken into account that the desired dynamic model of the system is used to generate tests. The test design procedure is simplified if the system model is linear, controllable, and observable. Based on this, the requirements for the links of the chains of the model are formulated. The proposed algorithm makes it possible to obtain a discrete-event model for the system characterized by a reduced amount of used diagnostic information.

1. Bhandari G.P., Gupta R. Fault analysis of service-oriented systems: a systematic literature review. IET Software, 2018, vol. 12, no. 6, pp. 446–460. https://doi.org/10.1049/iet-sen.2018.5249

2. Khalastchi E., Kalech M. Fault detection and diagnosis in multi-robot systems: A Survey. Sensors, 2019, vol. 19, no. 18, pp. 4019. https://doi.org/10.3390/s19184019

3. Shumsky A.Ye., Zhirabok A.N., Hadjiev C. Diagnosis and Fault Tolerant Control in Dynamic Systems. Vladivostok, Far Eastern Federal University, 2016, 178 p. (in Russian)

4. Britov G.S., Mironovskiy L.A. Accuracy and sensitivity of terminal diagnostics of controlled dynamic systems. Informatsionno-Upravliaiushchie Sistemy, 2019, no. 4, pp. 29–37. (in Russian). https://doi.org/10.31799/1684-8853-2019-4-29-37

5. Speranskii D.V. Lectures on Theory of Experiments with Finite Automata. Moscow, INTUIT Publ., 2016, 354 p. (in Russian)

6. Jung D., Khorasgani H., Frisk E., Krysander M., Biswas G. Analysis of fault isolation assumptions when comparing model-based design approaches of diagnosis systems. IFAC-PapersOnLine, 2015, vol. 48, no. 21, pp. 1289–1296. https://doi.org/10.1016/j.ifacol.2015.09.703

7. Krokavec D., Filasová A. Regular approach to additive fault detection in discrete-time linear descriptor systems. Studies in Systems, Decision and Control, 2021, vol. 313, pp. 61–74. https://doi.org/10.1007/978-3-030-58964-6_5

8. Han W., Wang Z., Shen Y., Liu Y. H-/L∞ fault detection for linear discrete-time descriptor systems. IET Control Theory & Applications, 2018, vol. 12, no. 15, pp. 2156–2163. https://doi.org/10.1049/iet-cta.2017.1408

9. Burdonov I.B., Kossatchev A.S. Testing of automata system. Proceedings of the Institute for System Programming of the RAS, 2016, vol. 28, no. 1, pp. 103–130. (in Russian). https://doi.org/10.15514/ISPRAS-2016-28(1)-7

10. Burdonov I., Kossatchev A. Automata system: determinism conditions and testing. Proceedings of the Institute for System Programming of the RAS, 2016, vol. 28, no. 1, pp. 151–184. (in Russian). https://doi.org/10.15514/ISPRAS-2016-28(1)-9

11. Li B., Khlif-Bouassida M., Toguyéni A. On-the-fly diagnosability analysis of bounded and unbounded labeled petri nets using verifier nets. International Journal of Applied Mathematics and Computer Science, 2018, vol. 28, no. 2, pp. 269–281. https://doi.org/10.2478/amcs-2018-0019

12. Fanti M.P., Mangini A.M. Ukovich W. Fault detection by labeled Petri nets in centralized and distributed approaches. IEEE Transactions on Automation Science and Engineering, 2013, vol. 10, no. 2, pp. 392–404. https://doi.org/10.1109/TASE.2012.2203596

13. Cabasino M.P., Giua A., Lafortune S., Seatzu C. A new approach for diagnosability analysis of Petri nets using verifier nets. IEEE Transactions on Automatic Control, 2012, vol. 57, no. 12, pp. 3104–3117. https://doi.org/10.1109/TAC.2012.2200372

14. Cabasino M.P., Giua A., Paoli A., Seatzu C. Decentralized diagnosis of discrete-event systems using labeled Petri nets. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2013, vol. 43, no. 6, pp. 1477–1485. https://doi.org/10.1109/TSMC.2013.2244208

15. Gruzlikov A.M., Kolesov N.V. Discrete-event diagnostic model for a distributed computational system. independent chains. Automation and Remote Control, 2016, vol. 77, no. 10, pp. 1805–1817. https://doi.org/10.1134/S0005117916100076

16. Gruzlikov A.M., Kolesov N.V. Discrete-event diagnostic model for a distributed computational system. Merging chains. Automation and Remote Control, 2017, vol. 78, no. 4, pp. 682–688. https://doi.org/10.1134/S0005117917040099

17. Lukoyanov E.V., Gruzlikov A.M. Hierarchical diagnostic model synthesis for dataflow real-time computing system. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2020, vol. 20, no. 5, pp. 677–682. (in Russian). https://doi.org/10.17586/2226-1494-2020-20-5-677-682

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