Self-Healing Databases: AI Techniques for Automated System Recovery

Abstract

Self-healing databases represent a pivotal advancement in the realm of data management, where automated recovery mechanisms powered by artificial intelligence (AI) ensure continuous operation and resilience. Traditional database systems often encounter disruptions due to hardware failures, software bugs, or security breaches, resulting in downtime and potential data loss. This paper explores the integration of AI techniques—such as anomaly detection, predictive analytics, and reinforcement learning—to create self-healing capabilities within database infrastructures. These AI-driven mechanisms can autonomously diagnose, rectify, and optimize databases in real-time, minimizing human intervention and significantly reducing recovery time. By leveraging AI for dynamic resource allocation, fault detection, and error correction, self-healing databases can maintain system integrity, enhance performance, and fortify security. This study presents a comprehensive framework for AI-driven self-recovery, detailing the algorithms and methodologies that empower databases to adapt and recover from unforeseen disruptions autonomously.