Abstract

AI-driven predictive maintenance models provide an intelligent approach for improving the reliability, availability, and operational efficiency of distributed cloud and edge computing infrastructure. Conventional maintenance strategies often rely on periodic inspections or reactive responses after hardware and software failures occur, leading to service interruptions, increased maintenance costs, and inefficient resource utilization. This framework integrates artificial intelligence, machine learning, real-time monitoring, and predictive analytics to identify potential infrastructure failures before they significantly affect service performance. Operational parameters such as CPU utilization, memory consumption, temperature, power usage, network traffic, storage health, and application response time are continuously collected from cloud servers and edge nodes. Machine learning models analyze historical and real-time data to detect anomalies, identify degradation patterns, and predict potential failures. Time-series forecasting and deep learning techniques can estimate remaining useful life and support proactive maintenance scheduling