SplitZNS: Towards an Efficient LSM-Tree on Zoned Namespace SSDs

被引:8
作者
Huang, Dong [1 ]
Feng, Dan [1 ]
Liu, Qiankun [1 ]
Ding, Bo [1 ]
Zhao, Wei [1 ]
Wei, Xueliang [1 ]
Tong, Wei [1 ]
机构
[1] Huazhong Univ Sci & Technol, WNLO, Luoyu Rd 1037, Wuhan, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
Zoned Namespace; LSM-tree; garbage collection;
D O I
10.1145/3608476
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The Zoned Namespace (ZNS) Solid State Drive (SSD) is a nascent form of storage device that offers novel prospects for the Log Structured Merge Tree (LSM-tree). ZNS exposes erase blocks in SSD as append-only zones, enabling the LSM-tree to gain awareness of the physical layout of data. Nevertheless, LSM-tree on ZNS SSDs necessitates Garbage Collection (GC) owing to the mismatch between the gigantic zones and relatively small Sorted String Tables (SSTables). Through extensive experiments, we observe that a smaller zone size can reduce data migration in GC at the cost of a significant performance decline owing to inadequate parallelism exploitation. In this article, we present SplitZNS, which introduces small zones by tweaking the zone-to-chip mapping to maximize GC efficiency for LSM-tree on ZNS SSDs. Following the multi-level peculiarity of LSM-tree and the inherent parallel architecture of ZNS SSDs, we propose a number of techniques to leverage and accelerate small zones to alleviate the performance impact due to underutilized parallelism. (1) First, we use small zones selectively to prevent exacerbating write slowdowns and stalls due to their suboptimal performance. (2) Second, to enhance parallelism utilization, we propose SubZone Ring, which employs a perchip FIFO buffer to imitate a large zone writing style; (3) Read Prefetcher, which prefetches data concurrently through multiple chips during compactions; (4) and Read Scheduler, which assigns query requests the highest priority. We build a prototype integrated with SplitZNS to validate its efficiency and efficacy. Experimental results demonstrate that SplitZNS achieves up to 2.77x performance and reduces data migration considerably compared to the lifetime-based data placement.
引用
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页数:26
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