Argus: A Precise and Efficient Resemblance Detection for Post-Deduplication Delta Compression

For data reduction techniques used in storage systems, delta compression is often implemented after deduplication, having been shown to achieve a much higher compression ratio by efficiently detecting and compressing similar data chunks. Unfortunately, existing resemblance detection approaches cannot maintain both high throughput and data reduction ratio simultaneously since they either introduce heavy calculation overhead or generate useless features that reduce the accuracy of resemblance detection. In this article, we propose Argus, a fast and precise resemblance detection approach using two primary techniques to improve data-reduction efficiency significantly. First, Argus utilizes a Bin-Wise Partitioning strategy, which separates the rolling hash values of each data chunk into different subsets according to the suffix bits of the hash value and generates features from these subsets. Thus, Argus generates features more efficiently, which both achieves high detection accuracy and improves feature generation speed. Second, based on the efficient Bin-Wise Partitioning strategy, Argus utilizes fine-grained Gear rolling hash and a Plain Feature strategy to manage the granularity of the content represented in the feature, increasing the probability of feature matching and catching as many similar chunks as possible. Consequently, Argus can achieve better detection accuracy, resulting in a much higher compression ratio than previous works while minimizing the computational overhead for resemblance detection. Our evaluation results driven by several real-world datasets suggest that, compared to the state-of-the-art approaches, Argus achieves up to 1.64 × (DeepSketch) and 2.29 × (Finesse, Odess, and N-Transform) higher delta compression ratio and achieves up to 19.9 × (N-Transform), 5.57 × (Finesse), and 1.18 × (Odess) faster feature generation speed.