Integrated load balancing algorithm in distributed information systems based on decision making theory methods

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Abstract

The paper considers the possibility of applying decision theory methods to solve the problem of load balancing in distributed information systems. A review and analysis of methods for evaluating and comparing multi-criteria alternatives is carried out, their advantages and disadvantages are shown in the context of the problem being solved. The most effective methods have been selected. Authors proposed a method for solving the balancing problem based on the integration of decision-making and machine learning methods. An example of using the proposed methodology is considered.

About the authors

A. V. Vishnekov

National Research University Higher School of Economics

Author for correspondence.
Email: avishnekov@hse.ru

Dr. Sc., Professor

Russian Federation, Moscow

E. M. Ivanova

National Research University Higher School of Economics

Email: emivanova@hse.ru

PhD, Associate Professor

Russian Federation, Moscow

A. A. Ladovir

National Research University Higher School of Economics

Email: aaladovir@edu.hse.ru

Master Student

Russian Federation, Moscow

A. V. Tsydypov

National Research University Higher School of Economics

Email: avtsydypov@edu.hse.ru

Master Student

Russian Federation, Moscow

References

  1. Bershadsky A. M., Kurilov L. S., Finogeev A. G. Research of load balancing strategies in distributed data processing systems, Izvestiya vuzov. The Volga region. Technical sciences, 2009, no. 4, available at: https://cyberleninka.ru/article/n/issledovanie-strategiy-balansirovki-nagruzki-v-sistemah-raspredelennoy-obrabotki-dannyh (date of request: 04/24/2025).
  2. Khaing M. T., Lupin S. A., Thu A. Evaluation of the effectiveness of load balancing methods in distributed computing systems, International Journal of Open Information Technologies, 2021, no. 11, available at: https://cyberleninka.ru/article/n/otsenka-effektivnosti-metodov-balansirovki-nagruzki-v-raspredelennyh-vychislitelnyh-sistemah (accessed: 04/24/2025).
  3. Ponomarenko D. N. Review of load balancing algorithms in cloud computing systems, Young scientist, 2024, no. 12 (511), pp. 21—30, available at: https://moluch.ru/archive/511/112232/ (date of access: 04/24/2025).
  4. Kuzmina P. O., Legenkova N. M. The Evolution of Banking Business Products: From Traditional Solutions to Digital Innovations, Trends and prospects of the banking system development in modern economic conditions: materials of the VI All-Russian Scientific and Practical Conference with international participation (Bryansk, December 26, 2024), vol. 1, Bryansk, 2025, 272 p., available at: https://brgu.ru/science/publikatsii/sborniki-trudov/Tendencii_i_perspektivy_razvitija_bankovskoj_sistemy_%D0%A21_2024.pdf#page=272 (date of request: 05/03/2025).
  5. Arkhiptsev E. D., Mokretsov N. S. Load balancing methods in information systems, News of higher educational institutions. Instrument engineering, 2024, vol. 67, no. 4, pp. 345—351, available at: https://doi.org/10.17586/0021-3454-2024-67-4-345-351
  6. Semko A. E., Gavrilova Yu. S. Overview of server load balancing algorithms for a pipeline automation project, Proceedings of the XVII International Scientific and Practical Conference "Development of Science and Practice in a globally changing world at risk", March 2023, pp. 345—349.
  7. Metel V. E., Kosukhina K. M. Methods of resource management in distributed computing systems, Concepts of development and effective use of scientific potential of society: collection of articles of the International Scientific and Practical Conference (March 15, 2025), Ufa, Aeterna, 2025, p. 5, available at: https://aeterna-ufa.ru/sbornik/NK-659.pdf#page=5 (accessed: 05/03/2025).
  8. Lyashov E. I. Resource-efficient load balancing algorithms in distributed microservice architectures, Bulletin of Science, 2025, vol. 1, no. 2 (83), pp. 629—647.
  9. Kudabaev T. K. Dynamic load balancing methods for distributed systems, Internauka: electron. scientific Journal, 2021, no. 19(195), available at: https://internauka.org/journal/science/internauka/195 (accessed: 05/07/2025).
  10. Lamanovskiy M. N., Lavrov D. N. Load balancing of cloud computing, Mathematical Structures and modeling, 2024, no. 2 (70), pp. 87—99, available at: https://cyberleninka.ru/article/n/balansirovka-nagruzki-oblachnyh-.
  11. Garcla-Valls M., Palomar-Cosln E. An Evaluation Process for IoT Platforms in Time-Sensitive Domains, Sensors (Basel), 2022, Vol. 22, No. 23, p. 9501. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC9737625/ (date of access: 05/07/2025)
  12. Halang W. A., Gumzej R., Colnaric M., Druzovec M. Measuring the Performance of Real-Time Systems, Real-Time Systems, 2000, vol. 18, no. 1, pp. 59—68, available at: https:// www.researchgate.net/publication/225260236_Measuring_the_Performance_of_Real-Time_Systems (date of request: 05/07/2025).
  13. Lobanov A. A. Preference method as a decision support tool, Prospects of Science and Education, 2015, no. 2 (14), pp. 36—43.
  14. Metlova A. V., Astakhova T. S., Sysoev A. N. The place and role of lexicographic support in information search using hierarchical classifications, NTI. Ser. 2. INFORM. PROCESSES AND SYSTEMS, 2022, no. 3, pp. 23—30, doi: 10.36535/0548-0027-2022-03-3
  15. Sadjadi S. J., Habibian M., Khaledi V. A multi-objective decision making approach for solving quadratic multiple response surface problems, Intern. J. Contemp. Math. Sci., 2008, vol. 3, no. 32, pp. 1595—1606.
  16. Batishchev D. I., Shaposhnikov D. E. Multicriteria selection based on individual preferences, Nizhny Novgorod, IPF RAS, 1994, 92 p.
  17. Song Yu, Xiaoli He, Binsack Ralf V., Zhihan Lv. A Probability Based Load Balancing Algorithm Applicable to MANET, Intern. J. of Systemics, Cybernetics and Informatics, 2013, vol. 17, no. 5, available at: https://ijssst.info/Vol-17/No-5/paper4.pdf (date of request: 04/21/2025).
  18. Kochkina M. V., Karamyshev A. N., Makhmutov I. I., Isavnin A. G., Rozentsvaig A. K. Analysis of multi-criteria methods of managerial decision-making (on the example of the problem of choosing suppliers of material and technical resources), Naberezhnye Chelny, Publishing and Printing Center NCI K(P) FU, 2017, 31 p.

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