AI-Driven Predictive Resilience in Multi-Cloud Environments

Abstract

This article introduces a novel AI-driven framework designed to enhance resilience in multi-cloud environments by predicting infrastructure failures and resource constraints before they impact service availability. The article leverages advanced machine learning techniques, including anomaly detection and time-series forecasting, to analyze telemetry data across heterogeneous cloud providers and identify emerging failure patterns with sufficient lead time for preventive intervention. Through a graduated remediation approach that automatically triggers appropriate response actions via integration with cloud orchestration tools, the article significantly reduces incident resolution times and service disruptions compared to traditional reactive methods. The article demonstrates the framework's effectiveness across diverse failure scenarios while highlighting its capacity to improve resource utilization efficiency through predictive scaling and workload optimization. The article addresses key challenges in cross-provider monitoring, data normalization, and security considerations, providing organizations with a practical solution for unified resilience management. This article contributes valuable insights into predictive operations approaches and establishes a foundation for future innovations in cloud infrastructure resilience, ultimately enabling organizations to maintain more reliable services while reducing operational costs and management complexity in increasingly distributed environments.