Locality aware management on NAND flash-based main memory for in-memory database systems

Conventional database systems manage all data on hard disks, but due to a hard disk's frequent I/O operations, this kind of management exposes critical problems when data is huge or operations are complex and frequent. As the size of the main memory continues to increase, main memory architecture and management becomes the major research trend in big data processing. Thus, we propose an optimized NAND Flash-based main memory (NFMM) structure for in-memory database systems to achieve the goal of having DRAM like performance at the lower cost and power consumption of Flash memory. For this goal, a horizontal combination of DRAM and NAND Flash memory is designed as a main memory model for database applications. A stream buffer and a DRAM buffer are designed to compensate for the slow access latency of Flash memory. Its optimized management method is designed to enhance the accessing locality and manage the stream buffer by prefetching pages. To evaluate the performance, Redis and Yahoo! Cloud Service Benchmark (YCSB) are used. In our experiment, a stream buffer is used to improve the data transfer speed. The result shows that in the proposed system, the execution time can achieve only 1.16x to 1.21x slower on average. At the same time, optimized NAND Flash-based main memory with 40 entries of stream buffer reduces power consumption up to 25% compared to the DRAMbased main memory system.