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Data reduction has become increasingly important in storage systems due to the explosive growth of digital data in the world that has ushered in the big data era. One of the main challenges facing large-scale data reduction is how to maximally detect and eliminate redundancy at very low overheads. In this paper, we present DARE, a low-overhead deduplication-aware resemblance detection and elimination...
The explosive growth of digital content results in enormous strains on the storage systems in the cloud environment. The data deduplication technology has been demonstrated to be very effective in shortening the backup window and saving the network bandwidth and storage space in cloud backup, archiving and primary storage systems such as VM platforms. However, the delay and power consumption of the...
Existing de-duplication solutions in cloud backup environment either obtain high compression ratios at the cost of heavy de-duplication overheads in terms of increased latency and reduced throughput, or maintain small de-duplication overheads at the cost of low compression ratios causing high data transmission costs, which results in a large backup window. In this paper, we present SAM, a Semantic-Aware...
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