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http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/10370Повний запис метаданих
| Поле DC | Значення | Мова |
|---|---|---|
| dc.contributor.author | Семеріков, Сергій Олексійович | - |
| dc.contributor.author | Zubov, Dmytro | - |
| dc.contributor.author | Kupin, Andrey | - |
| dc.contributor.author | Kosei, Maxim | - |
| dc.contributor.author | Holiver, Vladyslav | - |
| dc.date.accessioned | 2024-07-11T17:12:50Z | - |
| dc.date.available | 2024-07-11T17:12:50Z | - |
| dc.date.issued | 2024-04-16 | - |
| dc.identifier.citation | Semerikov S. Models and Technologies for Autoscaling Based on Machine Learning for Microservices Architecture / Serhiy Semerikov, Dmytro Zubov, Andrey Kupin, Maxim Kosei, Vladyslav Holiver // Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Systems. Volume I: Machine Learning Workshop, Lviv, Ukraine, April 12-13, 2024 / Edited by: Vasyl Lytvyn, Agnieszka Kowalska-Styczen, Victoria Vysotska // CEUR Workshop Proceedings. – 2024. – Vol. 3664. – P. 316-330. – Access mode : https://ceur-ws.org/Vol-3664/paper22.pdf | uk |
| dc.identifier.issn | 1613-0073 | - |
| dc.identifier.uri | https://ceur-ws.org/Vol-3664/paper22.pdf | - |
| dc.identifier.uri | http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/10370 | - |
| dc.description | [1] E. Zharikov, S. Telenyk, O. Rolik, Method of Distributed Two-Level Storage System Management in a Data Center Advances in Intelligent Systems and Computing, 938 (2020) 301–315. DOI:10.1007/978-3-030-16621-2_28. [2] P. Raj, A. Raman, H. Subramanian, Architectural Patterns. Packt Publishing (2017). ISBN: 9781787287495. [3] S.Newman, Building microservices: Designing fine-grained systems. Beijing i pozostałe: O’Reilly, 2021. ISBN: 978-1492034025. [4] M. Bruce, P. Pereira, Microservices in action. Shelter Island, NY: Manning Publications Co., 2019. ISBN: 9781617294457.[5] A. Müller, S. Guido, Introduction to machine learning with python: A guide for data scientists. Sebastopol: O’Reilly Media, 2018. ISBN: 978-1-449-36941-5. [6] J. Mueller, Machine learning security principles: Use various methods to keep data, networks, users, and applications safe from Prying eyes. Birmingham: Packt Publishing, 2023. ISBN: 978-1-80461-885-1. [7] S. Raschka, Y. Liu, and V. Mirjalili. Machine learning with pytorchand Scikit-Learn: Develop machine learning and deep learning models with python. Birmingham: Packt Publishing, 2022. ISBN: 978-1-80181-931-2. [8] M. Abouahmed, and O. Ahmed. Machine learning in microservices: Productionizing Microservices Architecture for Machine Learning Solutions. Birmingham: Packet Publishing, 2023. ISBN: 978-1-80461-774-8. [9] Ubuntu server - for scale out workloads Ubuntu, 2023. URL: https://ubuntu.com/server/ [10] A. Kupin, Y. Osadchuk, R. Ivchenko, O. Gradovoy. The Methods for Training Technological Multilayered Neural Network Structures (2021), in: CEUR Workshop Proceedings, 3013, pp. 327–333. URL: https://ceur-ws.org/Vol-3013/20210327.pdf. [11] J. Brains, PyCharm: The python IDE for professional developers by jetbrains, JetBrains, 2021. URL: https://www.jetbrains.com/pycharm/ [12] DBeaver Community, 2023. URL: https://dbeaver.io/ [13] MySQL, 2023. URL: https://www.mysql.com/ [14] Accelerated Container Application Development, 2023 Docker. URL: https://www.docker.com/ | uk |
| dc.description.abstract | The subject of the research in the article is machine learning processes in web service systems used for providing online services. The subject of the study is methods and tools for auto-scaling these web services using machine learning. The evolution of web services, their structure including development history, scaling options, key concepts of microservices architecture, and general principles of artificial intelligence and machine learning are analyzed, providing an important foundation for understanding technological innovations and potential enhancements for web services. The most significant aspects of applying machine learning in microservices architecture are identified, including various design patterns and machine learning models, which form the basis for improving the efficiency and capabilities of complex systems. Relevant mathematical models and necessary software are proposed. | uk |
| dc.language.iso | en | uk |
| dc.subject | microservices architecture | uk |
| dc.subject | artificial intelligence | uk |
| dc.subject | machine learning | uk |
| dc.subject | deep learning | uk |
| dc.subject | SAGA | uk |
| dc.subject | CRUD | uk |
| dc.subject | CQRS | uk |
| dc.subject | API gateway | uk |
| dc.subject | circuit breaker | uk |
| dc.subject | Python | uk |
| dc.subject | containers | uk |
| dc.subject | Docker | uk |
| dc.subject | Ubuntu | uk |
| dc.title | Models and Technologies for Autoscaling Based on Machine Learning for Microservices Architecture | uk |
| dc.type | Article | uk |
| Розташовується у зібраннях: | Кафедра інформатики та прикладної математики | |
Файли цього матеріалу:
| Файл | Опис | Розмір | Формат | |
|---|---|---|---|---|
| paper22.pdf | 2.41 MB | Adobe PDF | Переглянути/Відкрити |
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