Automatisation de la structuration des logs pour le Cloud Computing

Logs are a fundamental component of modern computer systems. They enable the analysis and monitoring teams to understand any abnormal or malicious behavior that may have occurred. The continuous increase in the volume of logs generated by these systems made it unsuitable for manual inspection and represents a real challenge with regard to process automation. In order to process these data, several log-structuring solutions have been developed. In this article, we analyze the capabilities of two solutions in order to meet the challenges of Cloud Computing in terms of efficiency and effectiveness. Our work focuses on the impact of parameterization and preprocessing on the performance of these methods -- two important steps as they require human intervention, which is incompatible with with the automation of the log-structuring process.

USTEP: Structuration des logs en flux gr{â}ce {à} un arbre de recherche {é}volutif

Logs record valuable system information at runtime. They are widely used by data-driven approaches for development and monitoring purposes. Parsing log messages to structure their format is a classic preliminary step for log-mining tasks. As they appear upstream, parsing operations can become a processing time bottleneck for downstream applications. The quality of parsing also has a direct influence on their efficiency. Here, we propose USTEP, an online log parsing method based on an evolving tree structure. Evaluation results on a wide panel of datasets coming from different real-world systems demonstrate USTEP superiority in terms of both effectiveness and robustness when compared to other online methods.