AI-Powered Query Optimization in Multitenant Database Systems

Abstract

AI-powered query optimization in multitenant database systems represents a paradigm shift from traditional static approaches to adaptive frameworks that continuously learn and evolve. This comprehensive article explores how artificial intelligence techniques address the unique challenges inherent in environments where multiple clients share database infrastructure. The evolution from rule-based heuristics to machine learning models enables systems to dynamically adapt to tenant diversity, service level agreement variations, resource contention, and shifting workload patterns. Through reinforcement learning, neural networks for cardinality estimation, workload classification, and anomaly detection, these AI approaches deliver tangible benefits including autonomous database operations, improved performance isolation, predictive resource scaling, and cost optimization. The article examines real-world applications that demonstrate how AI-enhanced optimization transforms operational efficiency in multitenant environments and reduces administrative overhead. It concludes by exploring emerging directions such as cross-layer optimization, tenant-specific learning, federated learning, and human-AI collaboration that promise to extend these capabilities further, creating more holistic, adaptable systems capable of handling the complexity and diversity of shared database environments.