Advancing Log Analysis with Synthetic Data: Techniques, Application, and Challenges

Abstract

Log data is an essential resource for system monitoring, diagnosis, and optimization in a variety of disciplines in today's data-driven contexts. In order to establish a cohesive framework for improved analysis and decision-making, this study explores the integration of log data from several systems. The goal of this paper is to address the difficulties posed by the amount, complexity, and heterogeneity of log data by utilizing cutting-edge methods in machine learning, data standardization, and log parsing. By combining log data from several sources, the suggested structure allows for centralized analysis and storage while preserving context and data quality. The results demonstrate how integrated log data can improve system resilience and predictive analytics in addition to operational efficiency. This study will help to collect logs from a distributed system, which will provide us with a comprehensive insight of the system's behavior and performance. Because the logs contain a wide range of activities, including system events, errors, and performance indicators, they offer valuable information for understanding and enhancing distributed systems.