Genetic algorithm for solving the problem of scaling a cloud-oriented object of informatization

LAKHNO V.; BEREKE M.; ADILZHANOVA S.; CHUBAIEVSKYI V.; KRYVORUCHKO O.; DESIATKO A.; PALAGUTA K.

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Genetic algorithm for solving the problem of scaling a cloud-oriented object of informatization

Author(s)

LAKHNO V. National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine

BEREKE M. Abay Kazakh National Pedagogical University, Almaty, Kazakhstan

ADILZHANOVA S. Al-Farabi Kazakh National University, Almaty, Kazakhstan

CHUBAIEVSKYI V. Kyiv National University of Trade and Economics, Department of Software Engineering and Cybersecurity, Kyiv, Ukraine

KRYVORUCHKO O. Kyiv National University of Trade and Economics, Department of Software Engineering and Cybersecurity, Kyiv, Ukraine

DESIATKO A. Kyiv National University of Trade and Economics, Department of Software Engineering and Cybersecurity, Kyiv, Ukraine

PALAGUTA K. Kyiv National University of Trade and Economics, Department of Software Engineering and Cybersecurity, Kyiv, Ukraine

Abstract

This article discusses the problem of mathematical modeling performed in the process of choosing server platforms and the required additional amount of RAM for the deployment of additional virtual workstations in the cloud-oriented object of informatization, for example the cloud-oriented learning environment (COLE) of university. At the same time, a number of requirements are imposed on COLE virtual machines (VM). The requirements are primarily related to the required amount of RAM for the server infrastructure of the educational institution's private cloud and the minimization of the modernization overall cost. This paper proposes a modified genetic algorithm (MGA) to solve such a problem. The algorithm can be used to solve problems related to the scaling of the university COLE. As a special case, the problem of analyzing options for scaling virtual workstations of COLE users was considered. Unlike existing solutions, it was proposed to apply a modified coding method. In addition, it was proposed to use an elitist strategy. With use of such an approach, the best individuals are selected for the gene bank. The use of a gene bank made it possible to reduce the number of generations in the search for a solution associated with the selection of the necessary configuration option for the COLE server infrastructure according to the parameters of the required amount of RAM for the servers of the educational institution private cloud and at the same time minimizing the total cost of its modernization.

Publication Details

Page(s) 1693-1705

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 6, Year: 2022

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Keywords

Optimization, Virtual Workplace, Cloud-Oriented Learning Environment, Virtual Workstation, Server Equipment

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