Efficient Wear-Leveling-Aware Data Placement for LSM-Tree based key-value store on ZNS SSDs

Emerging Zoned Namespace (ZNS) is a new-style Solid State Drive (SSD) that manages data in a zoned manner, which can achieve higher performance by strictly obeying the sequential write mode in each zone and alleviating the redundant overhead of garbage collections. Unfortunately, flash memory usually has a serious problem with limited program/erase cycles. Meanwhile, inappropriate data placement strategy of storage systems can lead to imbalanced wear among zones, severely reducing the lifespan of ZNS SSDs. In this paper, we propose a Wear-Leveling-Aware Data Placement (WADP) to solve this problem with negligible performance cost. First, WADP employs a wear-aware empty zone allocation algorithm to quantify the resets of zones and choose the less-worn zone for each allocation. Second, to prevent long-term zone occupation of infrequently written data (namely cold data), we propose a wear-leveling cold zone monitoring mechanism to identify cold zones dynamically. Finally, WADP adopts a real-time I/O pressure-aware data migration mechanism to adaptively migrate cold data for achieving wear-leveling among zones. We implement the proposed WADP in ZenFS and evaluate it with widely used workloads. Compared with state-of-the-art solutions, i.e., LIZA and FAR, the experimental results show that WADP can significantly reduce the standard deviation of zone resets while maintaining decent performance.

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Publication Details

Journal
Journal of King Saud University - Computer and Information Sciences
Published
2024-08-08
DOI
https://doi.org/10.1016/j.jksuci.2024.102156
Citations
4
Primary Topic
Advanced Data Storage Technologies
Type
article
Field-Weighted Citation Impact
1.40
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article

Efficient Wear-Leveling-Aware Data Placement for LSM-Tree based key-value store on ZNS SSDs

Chaoshu Yang, Yunlin Tan, Mingjie Li, Runyu Zhang et al.
4 citations
Journal of King Saud University - Computer and Information Sciences
Advanced Data Storage Technologies
1.40
article

Efficient Wear-Leveling-Aware Data Placement for LSM-Tree based key-value store on ZNS SSDs

Chaoshu Yang, Yunlin Tan, Mingjie Li, Runyu Zhang, Lening Zhou
article en
4 citations

Abstract

Emerging Zoned Namespace (ZNS) is a new-style Solid State Drive (SSD) that manages data in a zoned manner, which can achieve higher performance by strictly obeying the sequential write mode in each zone and alleviating the redundant overhead of garbage collections. Unfortunately, flash memory usually has a serious problem with limited program/erase cycles. Meanwhile, inappropriate data placement strategy of storage systems can lead to imbalanced wear among zones, severely reducing the lifespan of ZNS SSDs. In this paper, we propose a Wear-Leveling-Aware Data Placement (WADP) to solve this problem with negligible performance cost. First, WADP employs a wear-aware empty zone allocation algorithm to quantify the resets of zones and choose the less-worn zone for each allocation. Second, to prevent long-term zone occupation of infrequently written data (namely cold data), we propose a wear-leveling cold zone monitoring mechanism to identify cold zones dynamically. Finally, WADP adopts a real-time I/O pressure-aware data migration mechanism to adaptively migrate cold data for achieving wear-leveling among zones. We implement the proposed WADP in ZenFS and evaluate it with widely used workloads. Compared with state-of-the-art solutions, i.e., LIZA and FAR, the experimental results show that WADP can significantly reduce the standard deviation of zone resets while maintaining decent performance.

Journal of King Saud University - Computer and Information SciencesVol. 36(7)
Guizhou University (CN)
Affordable and clean energy
Openalex Percentile: Top 18%
Advanced Data Storage Technologies
1.40
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Efficient Wear-Leveling-Aware Data Placement for LSM-Tree based key-value store on ZNS SSDs — Chaoshu Yang, Yunlin Tan, et al. · Journal of King Saud University - Computer and Information Sciences (2024) | TGRS Research Map | TGRS