Improving Data Reliability with AI-Based Fault Tolerance in Distributed Databases

Abstract

In today's data-driven landscape, the reliability of distributed databases is paramount for ensuring continuous availability and integrity of data across various applications. This paper explores the integration of artificial intelligence (AI) to enhance fault tolerance mechanisms in distributed database systems. We present a comprehensive analysis of existing fault tolerance techniques, highlighting their limitations in dynamic and large-scale environments. We propose a novel AI-based framework that employs machine learning algorithms for predictive maintenance and anomaly detection, enabling proactive identification of potential failures before they impact system performance. Through extensive experimentation and case studies, we demonstrate the effectiveness of our framework in significantly reducing downtime and improving data integrity. Our results indicate that AI-enhanced fault tolerance not only optimizes system reliability but also minimizes operational costs associated with data recovery processes. This research contributes to the growing body of knowledge on AI applications in database management, providing valuable insights for practitioners and researchers aiming to build resilient and robust distributed systems.