Performance Optimization Techniques for Microservice Architectures in High-Load Scenarios

Abstract

The article addresses existing methods for enhancing the performance of microservice architectures under high-load conditions, where stability and scalability are required to adapt to changing demands. The objective of the study is to systematize existing optimization methods. The methodological framework includes data analysis, a comparison of various approaches such as containerization, auto-scaling, and the use of frameworks for asynchronous request processing. The research was conducted based on an analysis of publicly available articles, providing a comprehensive examination of the topic. The analyzed studies demonstrate that implementing hybrid solutions incorporating machine learning for load forecasting and dynamic infrastructure configuration significantly improves performance. Additionally, the studies address the management of service states and interactions, which is critical for maintaining data integrity under high loads. The information presented in the article will be valuable for system architects, DevOps engineers, and cloud computing specialists working with resource-intensive services. These solutions enable the creation of scalable, reliable infrastructures capable of efficiently handling large volumes of real-time data. The conclusion confirms the necessity of a comprehensive approach to optimizing microservice systems, focusing on dynamic adaptation and the integration of new technologies.