A Hash-based Space-e cient Page-level FTL for Large-capacity SSDs | |
Fan Ni; Chunyi Liu; Yang Wang; Chengzhong Xu; Xiao Zhang; Song Jiang | |
2017 | |
会议日期 | 2017 |
会议地点 | Shenzhen,China |
英文摘要 | With increasing demands on performance and capacity of SSDs in the enterprise-scale storage, the concern about the inefficient use of the DRAM space in the SSD rises, especially for those using page-level FTL (Flash Translation Layer). In such an FTL, the address mapping scheme allows a logical page address (LPA) to be mapped to any physical page address (PPA) in the disk. Though it provides flexible address management and minimizes internal data movements, it requires a large address mapping table whose size is proportional to the capacity of the disk. With the increase of SSD’s capacity, the table can be too large to be held entirely in the DRAM buffer of the SSD, causing constantly accessing to the flash for the address translation. This performance penalty due to the buffer misses is particularly high with workloads of weak access locality and large working sets. In this paper, we propose a space-efficient page-level FTL using hash functions in the address translation, named Hash-based Pagelevel FTL, or HP-FTL in short, to address the concern. HP-FTL trades mapping flexibility with limited performance impact for high space efficiency allowing the entire table to fit in the buffer and eliminating translation misses. The experiment results show that HP-FTL can provide up to 2.6X throughput compared to DFTL, a representative page-level FTL, using the same amount of DRAM for buffering the table. Meanwhile, HP-FTL reduces the mapping table size to about 25% of the table space required by page-level mapping schemes, including DFTL, without having any buffer misses. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.siat.ac.cn:8080/handle/172644/12654] |
专题 | 深圳先进技术研究院_数字所 |
作者单位 | 2017 |
推荐引用方式 GB/T 7714 | Fan Ni,Chunyi Liu,Yang Wang,et al. A Hash-based Space-e cient Page-level FTL for Large-capacity SSDs[C]. 见:. Shenzhen,China. 2017. |
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