Virtual Booth

This project develops an AI-enhanced file server to address the limitations of tradisional file servers in academic settings, which often suffer from high latency, inefficient bandwidth usage, and reactive monitoring. By intergrating machine learning models, Isolation Forest and K-Means for real-time anomaly detection and predictive analytics within a containerized Flask-based architecture, the system enables proactive maintenance and deep operational insights. Using Pormetheus and Grafana for monitoring, the prototype demonstrates improved resources efficiency and early fault prediction, offering a scalable, cost-effective solution for modernizing digital infrastructure in educational institutions