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Mathematical Models for the Design of GRID Systems to Solve Resource-Intensive Problems

Author

Listed:
  • Valeriya V. Tynchenko

    (Department of Computer Science, Institute of Space and Information Technologies, Siberian Federal University, 660041 Krasnoyarsk, Russia
    Department of Computer Science and Computer Engineering, Institute of Computer Science and Telecommunications, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)

  • Vadim S. Tynchenko

    (Scientific and Educational Center “Artificial Intelligence Technologies”, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Information-Control Systems Department, Institute of Computer Science and Telecommunications, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia
    Department of Technological Machines and Equipment of Oil and Gas Complex, School of Petroleum and Natural Gas Engineering, Siberian Federal University, 660041 Krasnoyarsk, Russia)

  • Vladimir A. Nelyub

    (Scientific and Educational Center “Artificial Intelligence Technologies”, Bauman Moscow State Technical University, 105005 Moscow, Russia)

  • Vladimir V. Bukhtoyarov

    (Scientific and Educational Center “Artificial Intelligence Technologies”, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Department of Technological Machines and Equipment of Oil and Gas Complex, School of Petroleum and Natural Gas Engineering, Siberian Federal University, 660041 Krasnoyarsk, Russia)

  • Aleksey S. Borodulin

    (Scientific and Educational Center “Artificial Intelligence Technologies”, Bauman Moscow State Technical University, 105005 Moscow, Russia)

  • Sergei O. Kurashkin

    (Scientific and Educational Center “Artificial Intelligence Technologies”, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Information-Control Systems Department, Institute of Computer Science and Telecommunications, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia
    Laboratory of Biofuel Compositions, Siberian Federal University, 660041 Krasnoyarsk, Russia)

  • Andrei P. Gantimurov

    (Scientific and Educational Center “Artificial Intelligence Technologies”, Bauman Moscow State Technical University, 105005 Moscow, Russia)

  • Vladislav V. Kukartsev

    (Department of Computer Science, Institute of Space and Information Technologies, Siberian Federal University, 660041 Krasnoyarsk, Russia
    Scientific and Educational Center “Artificial Intelligence Technologies”, Bauman Moscow State Technical University, 105005 Moscow, Russia
    Department of Information Economic Systems, Institute of Engineering and Economics, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia)

Abstract

Artificial neural networks are successfully used to solve a wide variety of scientific and technical problems. The purpose of the study is to increase the efficiency of distributed solutions for problems involving structural-parametric synthesis of neural network models of complex systems based on GRID (geographically disperse computing resources) technology through the integrated application of the apparatus of evolutionary optimization and queuing theory. During the course of the research, the following was obtained: (i) New mathematical models for assessing the performance and reliability of GRID systems; (ii) A new multi-criteria optimization model for designing GRID systems to solve high-resource computing problems; and (iii) A new decision support system for the design of GRID systems using a multi-criteria genetic algorithm. Fonseca and Fleming’s genetic algorithm with a dynamic penalty function was used as a method for solving the stated multi-constrained optimization problem. The developed program system was used to solve the problem of choosing an effective structure of a centralized GRID system that was configured to solve the problem of structural-parametric synthesis of neural network models. To test the proposed approach, a Pareto-optimal configuration of the GRID system was built with the following characteristics: average performance–103.483 GFLOPS, cost–500 rubles per day, availability rate–99.92%, and minimum performance–51 GFLOPS.

Suggested Citation

  • Valeriya V. Tynchenko & Vadim S. Tynchenko & Vladimir A. Nelyub & Vladimir V. Bukhtoyarov & Aleksey S. Borodulin & Sergei O. Kurashkin & Andrei P. Gantimurov & Vladislav V. Kukartsev, 2024. "Mathematical Models for the Design of GRID Systems to Solve Resource-Intensive Problems," Mathematics, MDPI, vol. 12(2), pages 1-33, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:276-:d:1319315
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    References listed on IDEAS

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    4. Gianluca Nastasi & Valentina Colla & Silvia Cateni & Simone Campigli, 2018. "Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1545-1557, October.
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