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.

QLC SSDs have gained increasing popularity in cloud computing, PCs, and smartphones due to their low prices and high density, but they suffer from extremely limited endurance. Deduplication can convert redundant chunk writes into fine-grained metadata updates, thereby promising to alleviate QLC wear. Nevertheless, our observation shows that existing deduplication approaches cause even more I/Os than non-deduplication systems. We find that the amplification comes from two sources: (1) I/O amplification due to the mismatched granularity between SSD I/O size (e.g., 4--16 KiB page) and deduplication metadata I/O size, and (2) SSD garbage collection (GC) amplification due to deduplication metadata updates for eliminating redundant chunks (i.e., increment reference count). To address the above problem, this paper proposes H2C-Dedup, which employs two essential techniques. First, to address I/O amplification, cold2hot-heating technique utilizes a log-structured metadata I/O scheme, which ensures that the deduplication metadata is flushed until it is accumulated within I/O cache to match with the OS I/O granularity. Second, to address GC amplification, hot2cold-suppression technique divides metadata into hot (e.g., to store reference count) and cold (e.g., to store fingerprint) segments, delta-encoding the hot entries to ensure both hot and cold metadata will not be modified once they are durable. As a result, H2C-Dedup significantly reduces the deduplication-induced I/O and GC amplification. We implement H2C-Dedup based on F2FS. Extensive experiments on the FEMU platform using microbenchmarks and real-world traces indicate that H2C-Dedup can extend to at most 3.3× and 3.4× lifespan while accelerating 26% and 37% I/O performance compared to SmartDedup and HF-Dedupe.