AI/ML techniques for predictive maintenance of cloud infrastructure

Abstract

Cloud infrastructure plays a pivotal role in delivering uninterrupted computing services across the globe. However, unexpected failures in critical components such as cooling systems, networking hardware, and power supply units can result in significant downtime and economic loss. This paper investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for predictive maintenance in cloud data centers. By analyzing historical sensor data, system logs, and performance metrics, various ML models—ranging from classical approaches like Random Forests to deep learning models such as LSTM and Autoencoders—are deployed to predict potential failures and trigger maintenance alerts. The paper also explores challenges like data imbalance, real-time inference, and deployment at scale in cloud environments. Experimental evaluations demonstrate the efficacy of ML models in identifying fault patterns, thereby improving operational reliability, reducing maintenance costs, and minimizing service disruptions.