Intelligent Log Onboarding: A Machine Learning-Driven Approach for Automated Log Source Configuration and Integration in Large-Scale Enterprise Environments

Rekha Sivakolundhu, Deepak Nanuru Yagamurthy

Abstract: The exponential growth of log data in modern enterprises presents significant challenges for log management and analysis. Traditional manual log onboarding processes are time-consuming, error-prone, and often require specialized knowledge. This paper proposes a novel framework for intelligent log onboarding that leverages machine learning techniques to automate the configuration and integration of diverse log sources into centralized log management systems. The proposed framework encompasses automated log source discovery, log format identification, and log parsing configuration. Machine learning models are employed to analyze log patterns, extract relevant features, and classify log types. The framework also incorporates active learning strategies to continuously improve its performance based on user feedback and new log data. The effectiveness of the proposed approach is evaluated through extensive experiments on a diverse set of real-world log data. The results demonstrate significant improvements in onboarding efficiency, accuracy, and scalability compared to traditional manual methods. This research contributes to the advancement of automated log management and has the potential to transform the way enterprises handle their ever-growing log data.

Keywords: Log onboarding, Machine learning, Log management, Automated configuration, Log parsing, Log format identification, Active learning, Centralized log management, Large-scale enterprise