Analytical Review of Orchestration Methods for Distributed Computing Systems Under Conditions of Dynamic Scaling
In this paper, a study of orchestration methods is carried out in order to find the most optimal method for managing distributed computing systems. To solve this problem, a study was made of methods for analyzing distributed computing systems, as well as methods of orchestration. In the course of th...
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Springer Science and Business Media Deutschland GmbH
2024
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ir-123456789-291912024-11-01T08:05:48Z Analytical Review of Orchestration Methods for Distributed Computing Systems Under Conditions of Dynamic Scaling Shiriaev, E. M. Ширяев, Е. М. Bezuglova, E. S. Безуглова, Е. С. Valuev, G. V. Валуев, Г. В. Gladkova, N. A. Гладкова, Н. А. Cybernets Orchestration Distributed computing systems Machine learning TOSCA In this paper, a study of orchestration methods is carried out in order to find the most optimal method for managing distributed computing systems. To solve this problem, a study was made of methods for analyzing distributed computing systems, as well as methods of orchestration. In the course of the study, it was found that for building distributed computing systems with dynamic scaling, it is most optimal to use hybrid orchestration based on mathematical modeling of the system. As a solution, the use of hybrid orchestration based on OASIS TOSCA templates and Kubernets containers is proposed. 2024-11-01T08:04:58Z 2024-11-01T08:04:58Z 2024 Статья Shiriaev E., Bezuglova E., Valuev G., Gladkova N. Analytical Review of Orchestration Methods for Distributed Computing Systems Under Conditions of Dynamic Scaling // Lecture Notes in Networks and Systems. - 2024. - 1044 LNNS. - pp. 148 - 156. - DOI: 10.1007/978-3-031-64010-0_14 https://dspace.ncfu.ru/handle/123456789/29191 en Lecture Notes in Networks and Systems application/pdf Springer Science and Business Media Deutschland GmbH |
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Репозиторий |
| language |
English |
| topic |
Cybernets Orchestration Distributed computing systems Machine learning TOSCA |
| spellingShingle |
Cybernets Orchestration Distributed computing systems Machine learning TOSCA Shiriaev, E. M. Ширяев, Е. М. Bezuglova, E. S. Безуглова, Е. С. Valuev, G. V. Валуев, Г. В. Gladkova, N. A. Гладкова, Н. А. Analytical Review of Orchestration Methods for Distributed Computing Systems Under Conditions of Dynamic Scaling |
| description |
In this paper, a study of orchestration methods is carried out in order to find the most optimal method for managing distributed computing systems. To solve this problem, a study was made of methods for analyzing distributed computing systems, as well as methods of orchestration. In the course of the study, it was found that for building distributed computing systems with dynamic scaling, it is most optimal to use hybrid orchestration based on mathematical modeling of the system. As a solution, the use of hybrid orchestration based on OASIS TOSCA templates and Kubernets containers is proposed. |
| format |
Статья |
| author |
Shiriaev, E. M. Ширяев, Е. М. Bezuglova, E. S. Безуглова, Е. С. Valuev, G. V. Валуев, Г. В. Gladkova, N. A. Гладкова, Н. А. |
| author_facet |
Shiriaev, E. M. Ширяев, Е. М. Bezuglova, E. S. Безуглова, Е. С. Valuev, G. V. Валуев, Г. В. Gladkova, N. A. Гладкова, Н. А. |
| author_sort |
Shiriaev, E. M. |
| title |
Analytical Review of Orchestration Methods for Distributed Computing Systems Under Conditions of Dynamic Scaling |
| title_short |
Analytical Review of Orchestration Methods for Distributed Computing Systems Under Conditions of Dynamic Scaling |
| title_full |
Analytical Review of Orchestration Methods for Distributed Computing Systems Under Conditions of Dynamic Scaling |
| title_fullStr |
Analytical Review of Orchestration Methods for Distributed Computing Systems Under Conditions of Dynamic Scaling |
| title_full_unstemmed |
Analytical Review of Orchestration Methods for Distributed Computing Systems Under Conditions of Dynamic Scaling |
| title_sort |
analytical review of orchestration methods for distributed computing systems under conditions of dynamic scaling |
| publisher |
Springer Science and Business Media Deutschland GmbH |
| publishDate |
2024 |
| url |
https://dspace.ncfu.ru/handle/123456789/29191 |
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