Hybrid Cloud Computing Architectures for Enhancing Data Reliability Through AI

Abstract

As cloud computing infrastructures become increasingly complex and diversified, the demand for reliable data management systems has surged. This paper presents a novel approach to enhancing data reliability in hybrid cloud computing architectures by leveraging artificial intelligence (AI) techniques. The proposed AI-driven framework integrates predictive analytics, anomaly detection, and automated recovery mechanisms to address data consistency, fault tolerance, and system resilience in multi-cloud environments. By employing machine learning models for real-time monitoring and error prediction, this approach reduces system failures and downtime while optimizing resource allocation across private and public clouds. Experimental results demonstrate significant improvements in data reliability and operational efficiency. This research provides a foundation for developing more intelligent, adaptive cloud architectures capable of ensuring data integrity and service continuity in dynamic and distributed systems.