Model of reorganization of elements of a wireless computing cluster with an orbital arrangement of elements


DOI: 10.34759/trd-2023-128-19

Аuthors

Nestruev D. S.*, Borzov D. B.**

South-Western State University, 94, 50-let Oktyabrya str., Kursk, 305040, Russia

*e-mail: nestruev98@mail.ru
**e-mail: borzovdb@mail.ru

Abstract

A cluster is a modular multiprocessor system created on the ground of standard computing nodes connected by a high-speed communication medium. A typical cluster is a set of computers or processor cores under centralized control, which the user perceives as a single entity. The main characteristic of a computing cluster is fault tolerance. To ensure greater survivability, cluster elements must be able to move in space and be reserved. This concerns, first of all, the control element (Host) and the nodes in which the accumulated information is stored (Storage). Scientific novelty consists in the method of orbital dynamic reconfiguration of roles. This method allows distributing the cluster elements in orbits relative to the control element, which, in turn, ensures a better connection with the rest of the cluster elements. Comparison is performed by the simulation results. Modeling is carried out with the developed computer program. Parameter of the wireless computing cluster running time in an extraordinary situation (disappearance of a signal between cluster elements, change in the cluster element position, etc.) is used as the comparison parameter.

The article considers an algorithmic model for initializing a wireless computing cluster with dynamic reconfiguration of roles by the orbital method, which significantly increases the fault tolerance of the cluster. The authors performed the analysis and comparison of the results of the described algorithm operation with existing ones. The results of the analysis revealed that dynamic reconfiguration of roles allows increasing the fault tolerance of a wireless computing cluster due to the fact that any of the elements are able to act as the ICD control element.

Keywords:

wireless computing cluster, algorithmic model, fault tolerance, graph, initialization algorithm

References

  1. Vishnevskii V.M. Teoreticheskie osnovy proektirovaniya komp’yuternykh sistem. (Theoretical foundations of designing computer systems), Moscow, Tekhnosfera, 2003, 512 p.
  2. Volkov A.A., Petrova S.N., Ginzburg A.V., Ivanova N.A. et al. Informatsionnye sistemy i tekhnologii v stroitel’stve (Information systems and technologies in construction), Moscow, Moskovskii gosudarstvennyi stroitel’nyi universitet|, 2015, 424 p.
  3. Dmitriev V.T. Komponenty i tekhnologii, 2006, no. 12, pp. 132 — 135.
  4. Korzhuk V.M. Regional’naya informatika i informatsionnaya bezopasnost’, 2017, no. 4, pp. 468-469.
  5. Finogeev A.A., Finogeev A.G., Nefedova I.S. Trudy mezhdunarodnogo simpoziuma «Nadezhnost’ i kachestvo», 2016, no. 1, pp. 258-260.
  6. Aminova R.R. Vserossiiskaya nauchno-prakticheskaya konferentsiya «Novye tekhnologii, materialy i oborudovanie rossiiskoi aviakosmicheskoi otrasli — AKTO-2016»: sbornik dokladov. Kazan’, Akademiya nauk Respubliki Tatarstan, 2016, vol. 2, pp. 338-342.
  7. Ogorodnikova O.V. Vserossiiskaya nauchno-prakticheskaya konferentsiya «Aktual’nye problemy deyatel’nosti podrazdelenii UIS»: sbornik trudov. Voronezh, Nauchnaya kniga, 2018, pp. 127-130.
  8. Borzov D.B., Titov V.S. Parallel’nye vychislitel’nye sistemy (arkhitektura, printsipy razmeshcheniya zadach (Parallel computing systems (architecture, principles of task placement), Izd-vo LAP LAMBERT Academic Publishing GmbH & Co. KG, 2012, 152 p.
  9. Pal’guev D.A. Radiopromyshlennost’, 2021, vol. 31, no. 2, pp. 49-60. DOI: 10.21778/2413-9599-2021-31-2-49-60
  10. Osipova V.A., Dubinina K.S. Modelirovanie i analiz dannykh, 2019, no. 3, pp. 24-31.
  11. Anan’ev A.V, Ivannikov K.S., Filatov S.V. Trudy MAI, 2022, no. 125. URL: https://trudymai.ru/eng/published.php?ID=168188. DOI: 10.34759/trd-2022-125-16
  12. Pavlov A.N., Pavlov D.A., Umarov A.B. Trudy MAI, 2021, no. 120. URL: https://trudymai.ru/eng/published.php?ID=161425. DOI: 10.34759/trd-2021-120-11
  13. Borzov D.B., Koshelev M.A., Sokolova Yu.V. Trudy MAI, 2021, no. 117. URL: https://trudymai.ru/eng/published.php?ID=156284. DOI: 10.34759/trd-2021-117-13
  14. Kondrashin M.A., Arsenov O.Yu., Kozlov I.V. Trudy MAI, 2016, no. 89. URL: http://trudymai.ru/eng/published.php?ID=73411
  15. Osipov N.A., Shavin A.S., Tarasov A.G. Trudy MAI, 2017, no. 94. URL: http://trudymai.ru/eng/published.php?ID=81085
  16. Davidovic T., Teodorovic D., Selmic M. Bee colony optimization, Part I: The algorithm overview, Yugoslav Journal Of Operations Research, 2015, vol. 25 (1), pp. 33-56. DOI:10.2298/YJOR131011017D
  17. Dawood N., Dawood H., Rodriguez-Trejo S. et al. Visualising urban energy use: the use of LiDAR and remote sensing data in urban energy planning, Visualization in Engineering, 2017, vol. 5 (1). DOI: 10.1186/s40327-017-0060-3
  18. Fabian Bock, Monika Sester. Improving Parking Availability Maps using Information from Nearby Roads, Transportation Research Procedia, 2016, vol. 19, pp. 207-214. URL: https://doi.org/10.1016/j.trpro.2016.12.081
  19. Parkhurst J., Darringer J., Grundmann B. From single core to multi-core: preparing for a new exponential, Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design, 2019, no. 1, pp. 67–72. URL: https://doi.org/10.1016/j.ifacol.2016.08.029
  20. Perković T., Šolić P., Zargariasl H., Čoko D. et al. Smart Parking Sensors: State of the Art and Performance Evaluation, Journal of Cleaner Production, 2020, vol. l262, pp. 121181. URL: https://doi.org/10.1016/j.jclepro.2020.121181

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