Enhancing Data Integrity in Cloud Computing through Intelligent Redundancy Management

Abstract

The rapid proliferation of cloud-based services has heightened the need for robust data integrity mechanisms. This study introduces an innovative framework that employs intelligent redundancy management techniques to safeguard data against corruption and loss. By integrating machine learning algorithms with redundancy protocols, our approach dynamically adjusts redundancy levels based on real-time data analysis and threat assessments. Experimental results demonstrate a significant improvement in data integrity metrics compared to traditional methods. The framework not only enhances reliability but also optimizes storage efficiency, making it a vital contribution to the field of cloud computing. These findings underscore the importance of adaptive data management solutions in contemporary cloud environments.