A Behavior-Aware Caching Architecture for Web Applications Using Static, Dynamic, and Burst Segmentation

This work proposes a behavior-aware caching architecture that improves cache hit rates by up to 10.8% over LRU and 36% over LFU in large-scale web applications, reducing redundant traffic and alleviating backend server load. The architecture partitions the cache into three sections—static, dynamic, and burst—according to query reuse patterns derived from user behavior. Static queries remain permanently stored, dynamic queries have time-bound validity, and burst queries are detected in real time using a statistical monitoring mechanism to prioritize sudden, high-demand requests. The proposed architecture was evaluated through simulation experiments using real-world query logs (a one-month trace of 1.5 billion queries from a commercial search engine) under multiple cache capacity configurations ranging from 1000 to 100,000 entries and in combination with the Least Recently Used (LRU) and Least Frequently Used (LFU) replacement policies. The results show that the proposed architecture consistently achieves higher performance than the baselines, with the largest relative gains in smaller cache configurations and applicability to distributed and hybrid caching environments without fundamental design changes. The integration of user-behavior modeling and burst-aware segmentation delivers a practical and reproducible framework that optimizes cache allocation policies in high-traffic and distributed environments.