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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.19660 |
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| _version_ | 1866915417434357760 |
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| author | Xu, Shuhang Gu, Yunfei Liu, Linhui Wu, Chentao |
| author_facet | Xu, Shuhang Gu, Yunfei Liu, Linhui Wu, Chentao |
| contents | As flash-based Solid State Drive (SSD) arrays become essential to modern data centers, scaling these arrays to meet explosive data growth is a frequent and critical operation. However, the conventional wear-leveling (WL) paradigm applied during scaling suffers from a fundamental flaw: it ignores the non-linear relationship between wear and failure probability, potentially pushing the most vulnerable, aged disks towards premature failure. To address this critical issue at its root, we propose the Probability-Sensitive Wear Leveling (PS-WL) scheme, which shifts the optimization goal from balancing wear to directly balancing failure risk. At its core, PS-WL introduces an "effective lifetime" model derived from a realistic failure probability to more accurately assess disk lifetime. This model guides a PID controller for wear leveling operation, with a conservative zone minimizes performance overhead by restricting warm data migration. Comprehensive simulations validate the superiority of PS-WL over state-of-the-art methods. The results demonstrate that our approach significantly reduces performance overhead while, most critically, consistently and effectively lowering the aggregated array failure risk across diverse system configurations and workloads. This proves that by directly optimizing for reliability, PS-WL builds a scalable storage system that is, by design, fundamentally safer, more efficient, and more stable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19660 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PS-WL: A Probability-Sensitive Wear Leveling scheme for SSD array scaling Xu, Shuhang Gu, Yunfei Liu, Linhui Wu, Chentao Distributed, Parallel, and Cluster Computing As flash-based Solid State Drive (SSD) arrays become essential to modern data centers, scaling these arrays to meet explosive data growth is a frequent and critical operation. However, the conventional wear-leveling (WL) paradigm applied during scaling suffers from a fundamental flaw: it ignores the non-linear relationship between wear and failure probability, potentially pushing the most vulnerable, aged disks towards premature failure. To address this critical issue at its root, we propose the Probability-Sensitive Wear Leveling (PS-WL) scheme, which shifts the optimization goal from balancing wear to directly balancing failure risk. At its core, PS-WL introduces an "effective lifetime" model derived from a realistic failure probability to more accurately assess disk lifetime. This model guides a PID controller for wear leveling operation, with a conservative zone minimizes performance overhead by restricting warm data migration. Comprehensive simulations validate the superiority of PS-WL over state-of-the-art methods. The results demonstrate that our approach significantly reduces performance overhead while, most critically, consistently and effectively lowering the aggregated array failure risk across diverse system configurations and workloads. This proves that by directly optimizing for reliability, PS-WL builds a scalable storage system that is, by design, fundamentally safer, more efficient, and more stable. |
| title | PS-WL: A Probability-Sensitive Wear Leveling scheme for SSD array scaling |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2506.19660 |