Achieving Better Benefits via Flexible Feature Matching in Post-Deduplication Delta Compression
Cloud or distributed storage systems characterized by high data redundancy necessitate effective data reduction techniques to reduce storage costs. Post-deduplication delta compression has proven effective by eliminating both duplicated and similar yet non-duplicated chunks. However, existing approa...
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| Published in: | Proceedings - IEEE International Parallel and Distributed Processing Symposium pp. 998 - 1010 |
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| Main Authors: | , , , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
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IEEE
03.06.2025
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| ISSN: | 1530-2075 |
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| Abstract | Cloud or distributed storage systems characterized by high data redundancy necessitate effective data reduction techniques to reduce storage costs. Post-deduplication delta compression has proven effective by eliminating both duplicated and similar yet non-duplicated chunks. However, existing approaches often rely on fixed-feature matching for resemblance detection, which, while fast, may lead to lower reduction ratios and not robust benefits across various datasets. In this paper, we introduce BePro, a novel system that integrates Flexible Feature Matching (§IV-A) to achieve better benefits in post-deduplication delta compression. BePro employs Gain Filtering (§IV-B) to identify high-gain chunks while discarding low-gain similar chunks, ensuring robust benefits across different datasets. Additionally, BePro implements a new indexing structure, LSH-Delta (§IV-C), to search for similar chunks and utilizes Index Load Balancer (§IV-D) for efficient resemblance detection by exploiting the distribution characteristics of similar chunks. Furthermore, the Index Manager (§IV-E) skillfully manages memory space overhead, ensuring memory efficiency. We implemented a pipeline prototyping framework to facilitate the evaluation of BePro and other leading techniques. Extensive experiments demonstrate that BePro improves the data-reduction ratios by up to 1.15 \times-2.35 \times while achieving comparable speed. |
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| AbstractList | Cloud or distributed storage systems characterized by high data redundancy necessitate effective data reduction techniques to reduce storage costs. Post-deduplication delta compression has proven effective by eliminating both duplicated and similar yet non-duplicated chunks. However, existing approaches often rely on fixed-feature matching for resemblance detection, which, while fast, may lead to lower reduction ratios and not robust benefits across various datasets. In this paper, we introduce BePro, a novel system that integrates Flexible Feature Matching (§IV-A) to achieve better benefits in post-deduplication delta compression. BePro employs Gain Filtering (§IV-B) to identify high-gain chunks while discarding low-gain similar chunks, ensuring robust benefits across different datasets. Additionally, BePro implements a new indexing structure, LSH-Delta (§IV-C), to search for similar chunks and utilizes Index Load Balancer (§IV-D) for efficient resemblance detection by exploiting the distribution characteristics of similar chunks. Furthermore, the Index Manager (§IV-E) skillfully manages memory space overhead, ensuring memory efficiency. We implemented a pipeline prototyping framework to facilitate the evaluation of BePro and other leading techniques. Extensive experiments demonstrate that BePro improves the data-reduction ratios by up to 1.15 \times-2.35 \times while achieving comparable speed. |
| Author | Yang, Fengkui Zhou, Ke Bao, Liang Zhang, Dongying Mao, Bo Jiang, Weipeng Liu, Yuhan Li, Chunhua |
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| SubjectTerms | Benefit Delta Compression Flexible Feature Matching Index Resemblance Detection |
| Title | Achieving Better Benefits via Flexible Feature Matching in Post-Deduplication Delta Compression |
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