AI for Automated Database Tuning: Approaches, Challenges, and Future Directions

This paper explores how artificial intelligence (AI) is revolutionizing database performance management by automating complex tuning tasks that previously required human expertise. It discusses advanced techniques like reinforcement learning and neural networks, which enable databases to self-optimize configurations, predict efficient query plans, and anticipate performance issues before they arise. The research presents case studies of AI-powered solutions, including OtterTune's machine learning-based parameter optimization and Microsoft's Bao for query planning, demonstrating how AI can significantly reduce tuning time and enhance query performance. The paper also addresses challenges such as the "black box" nature of AI decisions and the need for initial training data. Looking ahead, it examines emerging trends like federated learning for collaborative tuning and lightweight AI for edge databases, highlighting a transformative shift towards autonomous, self-learning database systems.