AI-ENHANCED PREDICTIVE MAINTENANCE FRAMEWORK FOR CLOUD-NATIVE MICROSERVICES: AN ENTERPRISE ARCHITECTURE APPROACH

Abstract

This article presents a comprehensive framework for implementing AI-driven predictive maintenance in cloud infrastructure supporting microservices architecture. The proposed approach leverages machine learning algorithms to analyze infrastructure metrics, including CPU utilization, memory consumption, network latency, and disk I/O patterns, to predict potential system failures and performance degradation. By employing deep learning models trained on historical operational data, the system demonstrates enhanced capability in identifying patterns that precede infrastructure failures, enabling proactive maintenance interventions. The implementation results show significant improvements in system uptime, reduced maintenance costs, and enhanced operational efficiency compared to traditional reactive maintenance approaches. Furthermore, the article explores the integration challenges and provides architectural recommendations for enterprise-scale deployment. This article contributes to the growing body of knowledge in cloud infrastructure management by presenting an innovative approach that combines artificial intelligence with predictive maintenance strategies, ultimately leading to more resilient and cost-effective cloud operations.