Predictive Analytics for Data Reliability in Cloud Computing: An AI Perspective

Abstract

The rapid expansion of cloud computing has intensified the need for robust data reliability mechanisms to ensure the integrity and performance of cloud-based systems. Predictive analytics, powered by artificial intelligence (AI), presents a transformative approach to enhancing data reliability by forecasting potential failures and optimizing resource management. This paper explores the application of AI-driven predictive analytics to improve data reliability in cloud computing environments. We examine various AI techniques, including machine learning algorithms and deep learning models, for predicting data anomalies, system failures, and performance bottlenecks. Through a comprehensive review of recent advancements and empirical case studies, this study evaluates the effectiveness of these predictive models in identifying potential issues before they impact system reliability. The results highlight the benefits of integrating AI-driven predictive analytics into cloud infrastructure, offering insights into improved fault detection, proactive maintenance, and resource optimization. The paper concludes with a discussion on the practical implications, challenges, and future directions for leveraging predictive analytics to enhance data reliability in cloud computing.