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Models and Technologies for Autoscaling Based on Machine Learning for Microservices Architecture

Семеріков, Сергій Олексійович; Zubov, Dmytro; Kupin, Andrey; Kosei, Maxim; Holiver, Vladyslav

URI: https://ceur-ws.org/Vol-3664/paper22.pdf
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/10370

Date: 2024-04-16

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.

Description:

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