NAND flash memory is playing a key role in the revolution of storage systems due to its desirable features such as fast random read and high energy-efficiency. It has been extensively applied in mobile devices like smart phones and PDAs. With increasing capacity, throughput and durability, NAND flash memory based solid state disk (hereafter, flash SSD) has started replacing hard disk drive (HDD) in laptops and desktop systems. Employing high-end flash SSDs in server applications, however, is promising yet challenging. One of the challenges is that currently flash SSD cannot fully meet heavy random write requirements demanded by data-intensive enterprise applications like online transaction processing (OLTP) because of flash memory's inherent update/erasure mechanisms. In this thesis, to boost flash SSD random write performance, we develop a new cache management scheme called element-level parallel optimization (EPO), which buffers and reorders write requests so that element-level parallelism within the architecture of a flash SSD can be mostly utilized. Further, we evaluate the performance of the EPO scheme using a validated disk simulator with both synthetic benchmarks and real-world server-class traces. Experimental results demonstrate that EPO noticeably outperforms traditional least recently used (LRU) and a state-of-the-art flash write buffer management scheme block padding least recently used (BPLRU).