Gasa: A New Page Replacement Algorithm for NAND Flash Memory

Abstract

NAND flash memory has been widely used as storage medium in diverse environments due to its light weight, low power consumption, and high I/O performance. Page replacement is an important operation in NAND flash- based storage systems. However, traditional replacement algorithms designed for magnetic disks fail to meet the needs of NAND flash memory due to inherent features such as asymmetric I/O latencies and erase-before- write. In order to address this problem, this paper proposes a new page replacement algorithm, called Ghost buffer Assisted and Self-tuning Algorithm (GASA). GASA reduces expensive flash write operations by evicting cold clean pages preferentially and maintains reasonable buffer hit ratios via a ghost buffer. In addition, GASA is self-tuning due to the use of a simple learning scheme, thus adaptively matching different workloads and buffer sizes. Experimental results based on real-world OLTP traces demonstrate that GASA offers a good trade-off between the hit ratio and the flash write count, thus achieving better I/O performance than the state-of-the-art page replacement algorithms.