PREDICTIVE ANALYTICS FOR LINUX SYSTEM FAILURE: A COMPARATIVE STUDY OF STATISTICAL AND MACHINE LEARNING APPROACHES

This article investigates the application of predictive analytics techniques to anticipate and mitigate system failures in Linux environments. We analyze diverse data sources, including system logs, performance metrics, and error reports, to develop and compare multiple predictive models. Our methodology encompasses logistic regression, time-series analysis, and machine learning algorithms such as Random Forest and Support Vector Machines (SVM). Through rigorous evaluation, we assess these models based on their predictive accuracy, computational efficiency, and ease of implementation in real-world scenarios. The article includes three case studies from different sectors, demonstrating the practical benefits of predictive analytics in reducing system downtime and enabling proactive maintenance. Our findings indicate that while machine learning models, particularly Random Forest, exhibit superior predictive performance, simpler statistical approaches offer valuable insights with lower computational overhead. We propose a framework for implementing predictive analytics in Linux systems, addressing common challenges such as data quality issues and model scalability. This article contributes to the growing field of IT operations analytics by providing empirical evidence on the effectiveness of various predictive techniques and offering practical guidelines for organizations seeking to enhance their Linux system reliability through data-driven approaches.