Resource-aware sector translation layer for resource-sensitive nand flash-based storage systems

Abstract

As a need for high-density storage capacity increases on many high-end mobile devices such as smartphones, large NAND flash-based storage systems are more commonly used in such smart devices. For these storage systems, however, it becomes a challenge to use large NAND flash without incurring a large system overhead such as a large memory requirement. We propose a novel flash translation layer (FTL), called Resource-Aware Sector Translation Layer (RAST), which is optimized to reduce the memory footprint of an FTL for resource-sensitive storage systems. RAST is based on a hybrid mapping scheme which uses a group of blocks as a unit of mapping so that a small mapping table can cover a large number of blocks. RAST further saves the memory footprint by using an on-demand metadata management scheme which brings only recently accessed metadata into memory. RAST employs a sampling-based wear-leveling scheme which provides competitive wear-leveling performance with very small memory. Our experimental results show that RAST can achieve a good performance level for resource-constraint storage systems with the small memory footprint. For 32 GB NAND flash memory, RAST can achieve the write throughput of up to 57 MB/s using only 34 kB memory.