Enhancing Cloud Data Reliability through Machine Learning-Driven Monitoring Systems

Abstract

The increasing complexity and scale of cloud data infrastructures necessitate advanced monitoring systems to ensure data reliability and integrity. Traditional monitoring approaches often fall short in addressing dynamic and intricate failure patterns inherent in modern cloud environments. This paper explores the enhancement of cloud data reliability through machine learning-driven monitoring systems. We propose a comprehensive framework integrating several machine learning techniques—including anomaly detection, predictive analytics, and automated response mechanisms—to proactively manage and mitigate data reliability issues. Our framework leverages historical and real-time data to build robust predictive models that can identify potential failures before they occur, optimize resource allocation, and adapt to changing conditions. We evaluate the proposed system using performance metrics such as accuracy, precision, recall, and F1-Score, as well as response time and scalability. The results demonstrate significant improvements in fault detection rates, reduced downtime, and enhanced system resilience compared to traditional monitoring methods. This study provides a practical approach to advancing cloud data reliability and offers a foundation for future research into the integration of machine learning with cloud data management.