Dynamic Data Reliability Engineering in AI-Enabled Cloud Infrastructures

Abstract

In the evolving landscape of cloud computing, ensuring data reliability is crucial for maintaining operational efficiency and system integrity. This paper presents a novel approach to dynamic data reliability engineering within AI-enabled cloud infrastructures, focusing on adaptive mechanisms that leverage artificial intelligence to enhance data reliability. We introduce a framework that integrates AI-driven predictive analytics, real-time monitoring, and automated anomaly detection to proactively address data reliability challenges. The framework employs advanced machine learning algorithms, including Autoencoders, LSTM Networks, and Isolation Forests, to analyze and predict data anomalies and system failures. Our methodology involves a comprehensive evaluation of these AI models in various cloud environments, assessing their performance based on metrics such as accuracy, precision, recall, and computational efficiency. Results demonstrate significant improvements in early detection of potential issues, reduced false positives, and enhanced overall system reliability. This approach not only mitigates risks associated with data corruption and system failures but also optimizes resource allocation and performance management in cloud infrastructures. The paper concludes with a discussion on the implications of our findings for future research and practical applications, emphasizing the potential for AIdriven solutions to revolutionize data reliability engineering in cloud environments.