International Journal of Science, Engineering and TechnologyAuthors: Nazia Parvin, Raihan Chowdhury
Abstract: Disk failures pose a significant risk to the reliability and performance of enterprise IT infrastructures, often leading to data loss, operational disruptions, and financial losses. Traditionally, organizations have responded to disk failures reactively, addressing issues only after they occur. However, predictive analysis provides a proactive approach by using performance logs and advanced analytics to forecast disk failures before they happen. By analyzing key performance metrics such as disk temperature, I/O operations, error rates, and SMART data, predictive models can identify patterns and anomalies that indicate potential disk failures. This paper explores the process of predictive analysis for disk failures, focusing on the role of performance logs, the integration of machine learning models, and the automation of monitoring and reporting through scripting. It highlights the benefits of continuous monitoring, early failure detection, reduced downtime, and improved resource management. Through real-time predictive analysis, organizations can replace or repair failing disks proactively, ensuring higher system reliability and reducing the risk of catastrophic failures. Furthermore, the paper addresses the challenges of implementing predictive models, such as data quality, false positives, and scalability, while showcasing the advantages of automating predictive analysis to streamline IT operations.
DOI: https://doi.org/10.5281/zenodo.16153424
