Methods for detecting anomalies in microservices using statistical analysis

Abstract

The subject of the study is methods of detecting anomalies in microservices using statistical analysis. Microservices is a popular software development architecture that allows for flexible and scalable systems. However, due to their complexity, such systems can be vulnerable to various types of anomalies that can affect their performance and reliability. The goal of the work is an analytical review of existing methods of detecting anomalies in microservice systems using statistical analysis methods. Detection of anomalies is critical to ensure stable system operation and quick response to possible problems. To achieve the purpose, the following tasks are defined: review of methods for detecting anomalies in microservices; description of the principles of regression analysis, cluster analysis and the method of principal components; comparison of methods according to the criteria of efficiency, computational complexity, resistance to noise and adaptability; recommendations for choosing a method and the possibility of combining them; summary of results and identification of directions for future research. A method for detecting anomalies in microservices is considered, which includes regression analysis, cluster analysis, and the method of principal components (PCA). The results of the study confirmed that each method has its advantages and limitations. Regression analysis is effective in systems with clear trends, but less effective in complex and dynamic systems. Cluster analysis has proven to be robust to noise and capable of detecting both individual anomalies and groups of anomalous events but requires significant computational resources. The method of principal components (PCA) is a powerful tool for the analysis of high-dimensional data, but it has limitations in the high complexity of calculations and interpretation of results. Each of the considered methods has its pros and cons, so the study proposed a new method that would consist in combining them. The conclusions emphasize the importance of statistical analysis for monitoring microservice systems. Well-chosen data analysis techniques facilitate the detection of anomalies in complex environments such as microservices. The use of regression analysis, cluster analysis and the method of principal components allows you to get a deep insight into the operation of the system. However, for best results, it is recommended to combine different methods and analyze their results in the context of a specific system. This approach provides greater resistance to anomalies and faster response to them in microservice architectures.