Integrating AI into Data-Center Operations: Methods for Detection, Prediction, and Adaptive Control

The increasing complexity and scale of modern data centers generate operational environments where the ability to detect anomalies, anticipate failures, and optimize resource usage is becoming critically important. Recent advances in machine learning and artificial intelligence offer powerful techniques for extracting actionable insights from heterogeneous monitoring data, ranging from logs and metrics to event streams and security signals. In this contribution, we explore an AI-driven approach that can be applied to a specific large-scale computing facility hosted at INFN-CNAF in Bologna, as a precursor for methods for anomaly detection, predictive alerting and fault classification. This study can establish the methodological foundations for future integration with the INFN-CNAF Big Data Platform, which is expected to provide the unified data backbone for advanced operational analytics. The goal of this contribution is to highlight promising research directions and practical use cases where AI can provide measurable value to large distributed computing infrastructures.