Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Park, Jisung | - |
dc.contributor.author | Kim, Jeonggyun | - |
dc.contributor.author | Kim, Yeseong | - |
dc.contributor.author | Lee, Sungjin | - |
dc.contributor.author | Mutlu, Onur | - |
dc.date.accessioned | 2023-12-26T18:14:16Z | - |
dc.date.available | 2023-12-26T18:14:16Z | - |
dc.date.created | 2022-12-06 | - |
dc.date.issued | 2022-02-22 | - |
dc.identifier.isbn | 9781939133267 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/46868 | - |
dc.description.abstract | Data reduction in storage systems is becoming increasingly important as an effective solution to minimize the management cost of a data center. To maximize data-reduction efficiency, existing post-deduplication delta-compression techniques perform delta compression along with traditional data deduplication and lossless compression. Unfortunately, we observe that existing techniques achieve significantly lower data-reduction ratios than the optimal due to their limited accuracy in identifying similar data blocks. In this paper, we propose DeepSketch, a new reference search technique for post-deduplication delta compression that leverages the learning-to-hash method to achieve higher accuracy in reference search for delta compression, thereby improving data-reduction efficiency. DeepSketch uses a deep neural network to extract a data block's sketch, i.e., to create an approximate data signature of the block that can preserve similarity with other blocks. Our evaluation using eleven real-world workloads shows that DeepSketch improves the data-reduction ratio by up to 33% (21% on average) over a state-of-the-art post-deduplication delta-compression technique. © AST 2022.All rights reserved. | - |
dc.language | English | - |
dc.publisher | USENIX Association | - |
dc.title | DeepSketch: A New Machine Learning-Based Reference Search Technique for Post-Deduplication Delta Compression | - |
dc.type | Conference Paper | - |
dc.identifier.scopusid | 2-s2.0-85140408864 | - |
dc.identifier.bibliographicCitation | USENIX Conference on File and Storage Technologies, pp.247 - 263 | - |
dc.identifier.url | https://www.usenix.org/conference/fast22/presentation/park | - |
dc.citation.conferencePlace | US | - |
dc.citation.conferencePlace | Santa Clara | - |
dc.citation.endPage | 263 | - |
dc.citation.startPage | 247 | - |
dc.citation.title | USENIX Conference on File and Storage Technologies | - |
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