Authors: Albert Joshep
Abstract: Predictive maintenance is an emerging discipline that combines system telemetry, machine learning, and automation to preemptively identify and resolve failures in complex computing environments. This review explores the implementation of predictive maintenance in Solaris and Red Hat Enterprise Linux (RHEL) platforms two prominent Unix-based systems widely deployed across enterprise IT landscapes. By comparing architectural features, telemetry sources, and modeling techniques, the study highlights both the unique capabilities and challenges presented by each operating system. Solaris benefits from a robust fault management architecture (FMA), advanced diagnostics like DTrace, and SPARC hardware optimization, making it well-suited for hardware-level monitoring. Red Hat, on the other hand, excels in automation, scalability, and hybrid cloud compatibility through tools such as Red Hat Insights, Ansible, and Performance Co-Pilot. The article delves into key predictive modeling strategies including time-series forecasting, anomaly detection, and classification, utilizing methods ranging from ARIMA and Isolation Forests to neural networks. Integration and automation workflows are examined, showcasing how Unix-native tools and open-source frameworks are used to train, deploy, and act upon model predictions. Through case studies, the review quantifies the benefits of predictive maintenance, including reduced mean time to recovery (MTTR), enhanced SLA adherence, and cost savings. Finally, it discusses limitations such as data inconsistency, model drift, and cross-platform transferability, while outlining future directions including AI co-pilots, self-learning systems, and Predictive Maintenance-as-a-Service (PMaaS). By offering a detailed comparative analysis and strategic recommendations, this review serves as a practical guide for enterprises aiming to implement or enhance predictive maintenance in mixed Unix environments.
DOI: https://doi.org/10.5281/zenodo.15798515
