Database systems serve as critical infrastructure for big data, with their performance directly affecting the quality of service (QoS) for upper-layer applications. With the rapid advancement of storage hardware technologies, traditional database systems increasingly exhibit stability challenges under large-scale and high-concurrency workloads. Baseline evaluations reveal severe performance degradation under intensive workloads, characterized by sharp throughput decline and noticeable jitter. Analysis of key operational metrics identifies the root cause of instability in the database I/O read-write model, where delayed dirty page flushing and insufficient candidate pages for backend threads reduce overall system stability. To address these issues, this study abstracts the database I/O mechanism into a producer-consumer model centered on buffer management and identifies inherent functional coupling problems. A novel functionally decoupled I/O model is proposed, featuring optimizations to the dirty page flushing mechanism and candidate page allocation strategy, thereby enhancing the supply of clean pages. Furthermore, the improved NSGA-II algorithm is integrated into the flushing framework for multi-objective white-box parameter tuning. Comprehensive evaluations are conducted using TPC-C and sysbench benchmarks across multiple dimensions, including data scale, testing duration, concurrency levels, and read-write patterns, supplemented by ablation studies. Experimental results demonstrate that the proposed framework achieves significant improvements over baseline approaches and existing optimization approaches in throughput, stability, and latency under high-pressure scenarios.