Data Reliability Engineering in Hybrid Cloud Architectures: A Deep Learning Approach

Abstract

The increasing reliance on hybrid cloud architectures, which combine public and private cloud environments, has introduced new challenges in maintaining data reliability and ensuring seamless system performance. This paper explores the integration of deep learning techniques into Data Reliability Engineering (DRE) frameworks to enhance data consistency, fault tolerance, and recovery in hybrid cloud infrastructures. A deep learning approach, leveraging recurrent neural networks (RNNs) and convolutional neural networks (CNNs), is proposed for predictive failure detection, data redundancy optimization, and anomaly detection. By implementing predictive models, the system anticipates failures, reduces downtime, and improves data synchronization across cloud environments. Experimental results demonstrate a 30% reduction in data loss incidents and a 25% improvement in system uptime, showcasing the potential of AI-driven DRE strategies in hybrid cloud ecosystems. The study concludes that deep learning significantly improves data reliability by enhancing proactive monitoring and rapid fault recovery, ensuring the sustainability and efficiency of hybrid cloud architectures.