每年專案
摘要
The continuous increase in data volume has led to the adoption of shingled-magnetic recording (SMR) as the primary technology for modern storage drives. This technology offers high storage density and low unit cost but introduces significant performance overheads due to the read-update-write operation and garbage collection (GC) process. To reduce these overheads, data deduplication has been identified as an effective solution as it reduces the amount of written data to an SMR-based storage device. However, deduplication can result in poor data locality, leading to decreased read performance. To tackle this problem, this study proposes a data locality-aware deduplication technology, LaDy, that considers both the overheads of writing duplicate data and the impact on data locality to determine whether the duplicate data should be written. LaDy integrates with DiskSim, an open-source project, and modifies it to simulate an SMR-based drive. The experimental results demonstrate that LaDy can significantly reduce the response time in the best-case scenario by 87.3% compared with CAFTL on the SMR drive. LaDy achieves this by selectively writing duplicate data, which preserves data locality, resulting in improved read performance. The proposed solution provides an effective and efficient method for mitigating the performance overheads associated with data deduplication in SMR-based storage devices.
原文 | ???core.languages.en_GB??? |
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文章編號 | 127 |
期刊 | ACM Transactions on Embedded Computing Systems |
卷 | 22 |
發行號 | 5 s |
DOIs | |
出版狀態 | 已出版 - 9 9月 2023 |
指紋
深入研究「LaDy: Enabling Locality-aware Deduplication Technology on Shingled Magnetic Recording Drives」主題。共同形成了獨特的指紋。專案
- 2 已完成
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用於高效能機器學習框架之新興記憶體與儲存系統設計-於混合式記憶儲存平台上高效能資料索引系統設計以支援機器學習應用(2/3)
Chen, T.-Y. (PI)
1/08/23 → 31/07/24
研究計畫: Research
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