Lune

USENIX ATC2023顶会

Light-Dedup: A Light-weight Inline Deduplication Framework for Non-Volatile Memory File Systems

Jiansheng Qiu, Yanqi Pan, Wen Xia, Xiaojia Huang, Wenjun Wu, Xiangyu Zou, Shiyi Li, Yu Hua

出版方
2023年份
20被引次数
4顶会引用

摘要

Emerging NVM is promising to become the next-generation storage media. However, its high cost hinders its development. Recent deduplication researches in NVM file systems demonstrate that NVM's cost can be reduced by eliminating redundant data blocks, but their design lacks complete insights into NVM's I/O mechanisms.

We propose Light-Dedup, a light-weight inline deduplication framework for NVM file systems that performs fast block-level deduplication while taking NVM's I/O mechanisms into consideration. Specifically, Light-Dedup proposes Light-Redundant-Block-Identifier (LRBI), which combines non-cryptographic hash with a speculative-prefetch-based byte-by-byte content-comparison approach. LRBI leverages the memory interface of NVM to enable asynchronous reads by speculatively prefetching in-NVM data blocks into the CPU/NVM buffers. Thus, NVM's read latency seen by content-comparison is markedly reduced due to buffer hits. Moreover, Light-Dedup adopts an in-NVM Light-Meta-Table (LMT) to store deduplication metadata and collaborate with LRBI. LMT is organized in the region granularity, which significantly reduces metadata I/O amplification and improves deduplication performance.

Experimental results suggest Light-Dedup achieves 1.01-8.98× I/O throughput over the state-of-the-art NVM deduplication file systems. Here, the speculative prefetch technique used in LRBI improves Light-Dedup by 0.3-118%. In addition, the region-based layout of LMT reduces metadata read/write amplification from 19.35×/9.86× to 6.10×/3.43× in our hand-crafted aging workload.

问问这篇 Paper

智能体会读完全文。

Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。

可以从这些问题问起

智能体调用

Luneget_paper_fulltext

在 Lune 里问

免费开始,无需绑卡

引用它的顶会 Paper4

问问它们各自怎么用它

它引用的顶会 Paper9

相关 Paper

黄昏的海面,两侧是细线勾勒的悬崖