DEVELOPING EFFICIENT SERVER MONITORING SYSTEMS USING AI FOR REAL-TIME DATA PROCESSING

Abstract

This paper explores the development and implementation of AI-based server monitoring systems aimed at optimizing real-time data processing. Traditional server monitoring systems, reliant on static thresholds and manual oversight, often struggle to handle the scale and complexity of modern server environments. In contrast, AI-driven monitoring leverages machine learning and deep learning algorithms to analyze large volumes of data, enabling precise anomaly detection and predictive maintenance. The proposed AI-based system architecture incorporates components such as real-time data collection, preprocessing, and model deployment, which together enhance response times, improve anomaly detection accuracy, and reduce system downtime. This study discusses the comparative advantages of AI-based monitoring over traditional methods, highlights performance and efficiency gains, and addresses key considerations for scalability and adaptability in diverse server environments. The findings underscore AI’s role in advancing server monitoring from reactive to proactive management, providing a foundation for future research in autonomous monitoring solutions.