Authors: Farzana Ali
Abstract: AI-based monitoring systems for IT infrastructure have become essential in modern digital environments where organizations require continuous visibility, performance optimization, and rapid fault detection across complex and distributed systems. With the rapid expansion of cloud computing, virtualization, and microservices architectures, traditional monitoring approaches are no longer sufficient to manage large-scale IT infrastructures. Artificial intelligence enhances monitoring systems by enabling predictive analytics, anomaly detection, and automated incident response. This paper explores the architecture and core components of AI-driven monitoring systems, including data collection agents, real-time analytics engines, machine learning models, and visualization dashboards. It highlights how these systems improve system reliability, reduce downtime, and optimize resource utilization. The study also discusses key applications in cloud environments, enterprise IT systems, and network management. Furthermore, it examines critical challenges such as data overload, false positives, integration complexity, and security concerns. Emerging solutions such as edge-based monitoring, AIOps platforms, and self-healing systems are also analyzed. The findings emphasize that AI-based monitoring systems are crucial for ensuring efficient, secure, and resilient IT infrastructure management.
DOI: https://doi.org/10.5281/zenodo.20280271
