Authors: Ganapathi Basu
Abstract: The increasing operational complexity of multi-OS Unix environments comprising legacy and modern systems such as Solaris, AIX, HP-UX, Linux, and BSD poses significant challenges for traditional system troubleshooting methodologies. These environments demand high availability, rapid diagnostics, and platform-agnostic observability, which are difficult to achieve using manual scripting and OS-specific tools alone. This review examines how Artificial Intelligence (AI) augments system administration by enabling intelligent diagnostics, predictive monitoring, and automated remediation across heterogeneous Unix infrastructures.Beginning with an overview of Unix's architectural evolution and the interoperability challenges in multi-OS deployments, the article outlines the limitations of conventional troubleshooting practices, including shell-based diagnostics, tribal knowledge, and siloed toolsets. It then explores the application of AI techniques such as machine learning for anomaly detection, natural language processing for log interpretation, and reinforcement learning for adaptive, self-healing responses. AI enables powerful capabilities in log normalization, root cause analysis (RCA), and event correlation especially critical in reducing alert fatigue and accelerating fault isolation. Advanced use cases such as predictive failure detection, behavior modeling, and AI-enhanced capacity planning illustrate the potential of intelligent monitoring. The review further evaluates unified diagnostic platforms like Splunk and Dynatrace, cross-platform frameworks, and real-world AI deployments in multi-OS settings. Key deployment challenges such as data silos, model generalization, and explainability are addressed alongside recommendations for integration with ITSM and DevSecOps pipelines. Emerging trends including AI co-pilots for system administrators, AIOps automation, and observability-as-a-service reflect a future where AI transforms Unix operations from reactive maintenance to autonomous infrastructure resilience. The paper concludes by emphasizing the importance of augmented intelligence where human expertise is amplified, not replaced offering a practical roadmap for AI-driven modernization in Unix ecosystems
DOI: http://doi.org/
