AI-Driven Optimization of High-Availability SQL Server Infrastructures: Leveraging Machine Learning for Predictive Performance Tuning and Automated Disaster Recovery

Abstract

Ensuring database availability and resilience is a critical challenge in modern enterprise systems, where even brief downtime can result in significant financial and operational losses. Traditional high-availability (HA) and disaster recovery (DR) frameworks, while effective, are often reactive and require extensive manual intervention. This paper proposes an AI-driven framework that integrates machine learning with SQL Server’s high-availability architectures to enhance predictive performance tuning and automate disaster recovery processes. Drawing on real-world enterprise database administration experience across large-scale deployments, the study outlines methods for using machine learning algorithms to analyze historical performance logs, predict potential system failures, and proactively trigger automated remediation workflows. Experimental results demonstrate a 30% reduction in downtime and improved resource utilization through adaptive query optimization and automated failover testing. By bridging AI techniques with core database administration practices, this research highlights how organizations can achieve near-zero downtime, reduced administrative overhead, and enhanced system resilience. The findings contribute to the growing body of applied AI in infrastructure engineering and offer a practical roadmap for enterprises seeking to modernize their database ecosystems.