The Development of the Load Balancer and the Parallel Module for Managing Associatively Protected Map Databases

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In the modern world, where information security and big data processing are key aspects of many industries, the importance of effectively managing and ensuring the reliability of cartographic databases is growing. These databases serve as the foundation for a wide range of applications, including geographic information systems, transportation management, environmental modeling, and urban planning. With the increase in the volume of geodata and the expansion of their use in various industries and science, the requirements for processing speed, accessibility and, above all, the security of this data are increasing. Poor management or security vulnerabilities can lead to significant losses, both economically and socially. The article is devoted to the development of a load balancer and a parallel module for managing associatively protected cartographic databases. The work includes a description and analysis of the use of the PostgreSQL DBMS with the Npgsql and PostGIS extensions for working with associatively protected spatial data. The article presents a method of parallel programming using the TPL library of the C# language. Testing of the developed module revealed optimal operating conditions, showing an increase in performance depending on the configuration of the nodes. The results of the study can serve as the basis for further optimization and development of systems for processing and managing large volumes of associatively protected geodata.

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Sobre autores

Igor Vershinin

Kazan National Research Technical University named after A.N. Tupolev – KAI

Autor responsável pela correspondência
Email: isvershinin@kai.ru
Scopus Author ID: 55977774300

Candidate of Engineering, Associate Professor; Head of the Department of Computer Systems

Rússia, Kazan, Republic of Tatarstan

Bibliografia

  1. Raikhlin V.A., Vershinin I.S., Gibadullin R.F., Pystogov S.V. Reliable recognition of masked binary matrices. Connection to information security in map systems. Lobachevskiy Journal of Mathematics. 2013. Vol. 34. Pp. 319–325.
  2. Raikhlin V.A., Gibadullin R.F., Vershinin I.S., Pystogov S.V. Reliable recognition of masked cartographic scenes during transmission over the network. International Siberian Conference on Control and Communications (SIBCON). 2016. Pp. 1–5.
  3. Gibadullin R.F., Khabibullin R.R. Realization of AES cryptographic algorithm on the basis of different parallelization technologies. In: Modern strategies and digital transformations of sustainable development of society, education and science: Proceedings of the IV International Scientific and Practical Conference, Moscow, December 09, 2022. Moscow: Alef, 2022. Pp. 178–184.
  4. Luo W., Lai D., Ren B. et al. Dynamic load balancing algorithm for distributed database based on PI feedback. 3rd International Conference on Intelligent Design (ICID). 2022. Pp. 277–280.
  5. Kuo K.-S., Oloso A., Doan K. et al. Implications of data placement strategy to Big Data technologies based on shared-nothing architecture for geosciences. IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2016. Pp. 7605–7607.
  6. Gibadullin R.F., Vershinin I.S., Minyazev R.S. Realization of replication mechanism in PostgreSQL DBMS. International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). 2017. Pp. 1–6.
  7. Braude E. Incremental UML for agile development: Embedding UML class models in source code. IEEE/ACM 3rd International Workshop on Rapid Continuous Software Engineering (RCoSE). 2017. Pp. 27–31.
  8. Zhang M. PMT: A procedure migration tool from oracle to postgreSQL. IET International Conference on Smart and Sustainable City (ICSSC 2013). 2013. Pp. 391–396.
  9. Gibadullin R.F., Gashigullin D.A., Vershinin I.C. Development of StegoStream decorator for associative byte stream protection. Modeling, Optimization and Information Technologies. 2023. Vol. 11. No. 2. (In Rus.) URL: moitvivt.ru/ru/journal/pdf?id=1359

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2. Fig.1

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3. Fig. 2. Activity diagram of MPI process task definitions

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4. Fig. 3. Fragment with hidden geom_encrypt column of the results table

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