AI-Powered Fault Detection and Recovery in High-Availability Databases

Abstract

In the ever-evolving landscape of database management, high availability and fault tolerance are paramount for ensuring continuous service delivery and data integrity. This paper explores the application of artificial intelligence (AI) techniques in enhancing fault detection and recovery mechanisms within high-availability databases. By leveraging machine learning algorithms and predictive analytics, we propose a novel framework that not only identifies potential faults in real-time but also initiates proactive recovery processes. Our experimental results demonstrate a significant reduction in downtime, with the proposed AI-powered approach achieving an average fault detection rate of 95% and a recovery time reduction of 40% compared to traditional methods. Additionally, the framework's adaptability allows it to learn from historical fault patterns, improving its predictive capabilities over time. This research contributes to the ongoing discourse on the integration of AI in database management, highlighting its potential to enhance system resilience and reliability. The findings underscore the importance of implementing intelligent fault detection and recovery strategies as organizations increasingly rely on highavailability databases to support mission-critical applications.