Cloud Computing and Data Engineering: AI Approaches for Dynamic Fault Tolerance

Abstract

In the rapidly evolving landscape of cloud computing, dynamic fault tolerance has become a critical area of focus for ensuring uninterrupted service delivery and system reliability. This paper explores innovative AI-driven approaches to enhance dynamic fault tolerance in cloud environments, emphasizing the integration of machine learning algorithms and predictive analytics to preemptively identify and mitigate potential faults. We propose a novel framework that leverages AI to dynamically adjust fault tolerance mechanisms based on real-time system performance and predictive insights. The framework employs advanced techniques such as anomaly detection, automated recovery processes, and adaptive resource management to enhance system robustness and resilience. Through comprehensive experiments and simulations, we demonstrate the effectiveness of the proposed AI approaches in improving fault detection accuracy, reducing recovery time, and optimizing resource allocation. Our results show significant improvements in system uptime and performance, highlighting the potential of AI-driven dynamic fault tolerance to address the challenges of modern cloud computing environments. This study provides a valuable contribution to the field by offering a practical solution for enhancing fault tolerance in dynamic and complex cloud systems, paving the way for more reliable and resilient cloud infrastructures.