AI-Driven Fault-Tolerant System Design for Resilient Distributed Computing Environments

Abstract

As distributed computing environments grow in scale and complexity, ensuring system reliability and resilience against failures becomes a critical challenge. This paper presents an AIdriven fault-tolerant system design that leverages machine learning, predictive analytics, and selfhealing mechanisms to enhance the robustness of distributed computing frameworks. The proposed approach integrates real-time anomaly detection, proactive failure prediction, and automated recovery strategies to mitigate the impact of hardware and software failures. By utilizing graph-based fault propagation models, reinforcement learning for dynamic resource allocation, and federated learning for decentralized fault monitoring, the system adapts to diverse failure scenarios while optimizing performance. Experimental evaluations demonstrate that the AI-powered framework reduces system downtime by 45%, improves fault detection accuracy to 92%, and enhances overall system efficiency in cloud and edge computing environments. This research contributes to the development of next-generation resilient distributed systems capable of handling large-scale failures autonomously.