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Reducing Fragmentation for In-line Deduplication Backup Storage via Exploiting Backup History and Cache Knowledge.
- Source :
- IEEE Transactions on Parallel & Distributed Systems; Mar2016, Vol. 27 Issue 3, p855-868, 14p
- Publication Year :
- 2016
-
Abstract
- In backup systems, the chunks of each backup are physically scattered after deduplication, which causes a challenging fragmentation problem. We observe that the fragmentation comes into sparse and out-of-order containers. The sparse container decreases restore performance and garbage collection efficiency, while the out-of-order container decreases restore performance if the restore cache is small. In order to reduce the fragmentation, we propose History-Aware Rewriting algorithm (HAR) and Cache-Aware Filter (CAF). HAR exploits historical information in backup systems to accurately identify and reduce sparse containers, and CAF exploits restore cache knowledge to identify the out-of-order containers that hurt restore performance. CAF efficiently complements HAR in datasets where out-of-order containers are dominant. To reduce the metadata overhead of the garbage collection, we further propose a Container-Marker Algorithm (CMA) to identify valid containers instead of valid chunks. Our extensive experimental results from real-world datasets show HAR significantly improves the restore performance by 2.84-175.36 $\times$<alternatives><inline-graphic xlink:type="simple" xlink:href="fu-ieq1-2410781.gif"/></alternatives> at a cost of only rewriting 0.5-2.03 percent data. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 10459219
- Volume :
- 27
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Parallel & Distributed Systems
- Publication Type :
- Academic Journal
- Accession number :
- 113070563
- Full Text :
- https://doi.org/10.1109/TPDS.2015.2410781