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.
Authors
- Chaoshu Yang (ORCID: https://orcid.org/0000-0002-0690-7370)
- Yunlin Tan (ORCID: https://orcid.org/0000-0003-0244-162X)
- Mingjie Li (ORCID: https://orcid.org/0000-0003-0417-5089)
- Runyu Zhang (ORCID: https://orcid.org/0000-0003-3732-5098)
- Lening Zhou
Institutions
- Guizhou University (CN)
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