AI-Powered Automation in Cloud Data Reliability Engineering: A Hybrid Approach

Abstract

In the rapidly evolving landscape of cloud computing, ensuring data reliability is paramount for maintaining system performance and user trust. This paper introduces a hybrid approach to AIpowered automation in cloud data reliability engineering, combining machine learning, predictive analytics, and automated remediation strategies to enhance data integrity and system resilience. We present a novel framework that integrates anomaly detection algorithms, predictive maintenance models, and automated recovery mechanisms to address common challenges in cloud data environments. The proposed approach is evaluated through a series of experiments conducted on a large-scale cloud infrastructure, demonstrating significant improvements in data consistency, fault tolerance, and operational efficiency. The results indicate a 30% reduction in data inconsistencies, a 40% improvement in fault detection accuracy, and a 25% decrease in system downtime compared to traditional reliability engineering methods. This hybrid approach not only enhances the reliability of cloud data systems but also optimizes resource utilization and reduces operational costs. Our findings highlight the potential of AI-driven automation to transform data reliability engineering, offering a scalable and adaptive solution for managing complex cloud environments.