Stochastic models for time complexity of computing tasks: II. Description of interaction with databases

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The paper contains the second part of an investigation devoted to the design of the mathematical models for the execution time of user tasks carried out on the virtual calculating nodes. We provide the performance of the proposed model for the description of the data processing fulfilled in the databases. As a testbed for stress testing, we choose a prototype of the anonymization system of the passengers’ personal data. There are stochastic models describing two types of user tasks: personal data anonymization procedure and calculation of the sample statistical characteristics. The paper contains a detailed description of the stress test planning and fulfillment for both models. The obtained mathematical models developed by the real data demonstrate high performance.

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Sobre autores

A. Borisov

Computer Science and Control Federal Research Center of Russian Academy of Sciences

Autor responsável pela correspondência
Email: ABorisov@frccsc.ru
Rússia, Moscow

A. Ivanov

Computer Science and Control Federal Research Center of Russian Academy of Sciences

Email: AIvanov@frccsc.ru
Rússia, Moscow

Bibliografia

  1. Борисов А., Иванов А. Стохастическое модели трудоемкости вычислительных задач. I. Принципы формирования, сбор статистических данных, задачи идентификации // Изв. РАН. ТиСУ. № 1. С. 22–34.
  2. Борисов А., Босов А., Иванов А. Применение имитационного компьютерного моделирования к задаче обезличивания персональных данных. Модель и алгоритм обезличивания методом синтеза // Программирование. 2023. № 5. C. 19–34.
  3. Lagarias, J., Reeds J., Wright M., Wright P. Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions // SIAM Journal of Optimization. 1998. V. 9. Iss. 1. P. 112–147.
  4. Себер Дж. Линейный регрессионный анализ. М.: Мир, 1980. 456 с.
  5. https://wiki.astralinux.ru/kb/minimal-nye-i-rekomenduemye-sistemnye-trebovaniya-dlya-os-187796554.html.

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2. Figure 1. Projection of the definition area of the model of job 1

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3. Figure 2. Statistical data of load testing of task 1 for case 1 processor core

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4. Figure 3. Model cross-section for case 1 of the kernel

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5. Figure 4. Model cross-section for case 1 of the kernel

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6. Figure 5. Dependence on the job size for different fixed RAM sizes (1 core)

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7. Figure 6. Dependence on the job size for different fixed RAM sizes (1 core)

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8. Figure 7. Dependence on RAM size for different fixed job sizes (1 core)

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9. Figure 8. Dependence on RAM size for different fixed job sizes (1 core)

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10. Figure 9. Dependence on the number of processor cores for different fixed job sizes (RAM 4 GB)

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11. Figure 10. Projection of the definition area of the model of task 2

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12. Figure 11. Statistical data of load testing of task 2 for case 1 processor core

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13. Figure 12. Model cross-section for case 1 of the core

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14. Figure 13. Model cross-section for case 1 kernels

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15. Figure 14. Dependence on the job size for different fixed amounts of RAM and number of cores

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16. Figure 15. Job size dependency for different fixed RAM sizes and number of cores

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17. Figure 16. Dependence on RAM size for different fixed job sizes and number of cores

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18. Figure 17. Dependence on RAM size for different fixed job sizes and number of cores

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19. Figure 18. Dependence on the number of cores for different fixed job sizes and RAMs

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20. Figure 19. Dependence on the number of cores for different fixed job sizes and RAMs

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