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Content-Defined Chunking (CDC) has been playing a key role in data deduplication systems recently due to its high redundancy detection ability. However, existing CDC-based approaches introduce heavy CPU overhead because they declare the chunk cut-points by computing and judging the rolling hashes of the data stream byte by byte. In this article, we propose FastCDC, a Fast and efficient Content-Defined Chunking approach, for data deduplication-based storage systems. The key idea behind FastCDC is the combined use of five key techniques, namely, gear based fast rolling hash, simplifying and enhancing the Gear hash judgment, skipping sub-minimum chunk cut-points, normalizing the chunk-size distribution in a small specified region to address the problem of the decreased deduplication ratio stemming from the cut-point skipping, and last but not least, rolling two bytes each time to further speed up CDC. Our evaluation results show that, by using a combination of the five techniques, FastCDC is 3-12X faster than the state-of-the-art CDC approaches, while achieving nearly the same and even higher deduplication ratio as the classic Rabin-based CDC. In addition, our study on the deduplication throughput of FastCDC-based Destor (an open source deduplication project) indicates that FastCDC helps achieve 1.2-3.0X higher throughput than Destor based on state-of-the-art chunkers. [ABSTRACT FROM AUTHOR]

Chunk-level deduplication plays an important role in backup storage systems. Existing Content-Defined Chunking (CDC) algorithms, while robust in finding suitable chunk boundaries, face the key challenges of (1) low chunking throughput that renders the chunking stage a serious deduplication performance bottleneck, (2) large chunk size variance that decreases deduplication efficiency, and (3) being unable to find proper chunk boundaries in low-entropy strings and thus failing to deduplicate these strings. To address these challenges, this paper proposes a new CDC algorithm called the Asymmetric Extremum (AE) algorithm. The main idea behind AE is based on the observation that the extreme value in an asymmetric local range is not likely to be replaced by a new extreme value in dealing with the boundaries-shifting problem. As a result, AE has higher chunking throughput, smaller chunk size variance than the existing CDC algorithms, and is able to find proper chunk boundaries in low-entropy strings. The experimental results based on real-world datasets show that AE improves the throughput performance of the state-of-the-art CDC algorithms by more than $2.3\times$ , which is fast enough to remove the chunking-throughput performance bottleneck of deduplication, and accelerates the system throughput by more than 50 percent, while achieving comparable deduplication efficiency. [ABSTRACT FROM AUTHOR]

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 scheme that effectively exploits existing duplicate-adjacency information for highly efficient resemblance detection in data deduplication based backup/archiving storage systems. The main idea behind DARE is to employ a scheme, call Duplicate-Adjacency based Resemblance Detection (DupAdj), by considering any two data chunks to be similar (i.e., candidates for delta compression) if their respective adjacent data chunks are duplicate in a deduplication system, and then further enhance the resemblance detection efficiency by an improved super-feature approach. Our experimental results based on real-world and synthetic backup datasets show that DARE only consumes about 1/4 and 1/2 respectively of the computation and indexing overheads required by the traditional super-feature approaches while detecting 2-10 percent more redundancy and achieving a higher throughput, by exploiting existing duplicate-adjacency information for resemblance detection and finding the “sweet spot” for the super-feature approach. [ABSTRACT FROM PUBLISHER]