Machine Learning-Enhanced Database Cache Management: A Comprehensive Performance Analysis and Comparison of Predictive Replacement Policies

The exponential growth of data-driven applications has intensified performance demands on database systems, where cache management represents a critical bottleneck. Traditional cache replacement policies such as Least Recently Used (LRU) and Least Frequently Used (LFU) rely on simple heuristics that fail to capture complex temporal and frequency patterns in modern workloads. This research presents a modular machine learning-enhanced cache management framework that leverages pattern recognition to optimize database performance through intelligent replacement decisions. Our approach integrates multiple machine learning models—Random Forest classifiers, Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), and Gradient Boosting methods—within a modular architecture enabling seamless integration with existing database systems. The framework incorporates sophisticated feature engineering pipelines extracting temporal, frequency, and contextual characteristics from query access patterns. Comprehensive experimental evaluation across synthetic workloads, real-world production datasets, and standard benchmarks (TPC-C, TPC-H, YCSB, and LinkBench) demonstrates consistent performance improvements. Machine learning-enhanced approaches achieve 8.4% to 19.2% improvement in cache hit rates, 15.3% to 28.7% reduction in query latency, and 18.9% to 31.4% increase in system throughput compared to traditional policies and advanced adaptive methods including ARC, LIRS, Clock-Pro, TinyLFU, and LECAR. Random Forest emerges as the most practical solution, providing 18.7% performance improvement with only 3.1% computational overhead. Case study analysis across e-commerce, financial services, and content management applications demonstrates measurable business impact, including 8.3% conversion rate improvements and USD 127,000 annual revenue increases. Statistical validation (p0.8) confirms both statistical and practical significance.