Data deduplication technologies are widely exploited to reduce capacity demands for storage. Previous chunk-based offline deduplication technologies often cause serious performance overhead due to data chunking and indexing. Particularly, they are not efficient fornon-volatile memory(NVM) based storage systems because they cannot fully exploit the byte-addressability feature of NVMs for fine-grained deduplication. In this paper, we proposeI/O Causality based In-line Deduplication(ICID) to maximize the deduplication ratio for NVM-based storage systems. Unlike previous inline deduplication schemes that use hash indexes to identify duplicate data slices, ICID records memory-copy operations in a B-tree structure to achieve causality-based inline deduplication. We propose two novel techniques to manage memory-copy records in the B-tree efficiently. First, to speed up the B-tree lookup, we group memory-copy records targeted to the same page in a B-tree node to improve data locality. Second, we exploit the spatial locality of memory accesses to identify outdated memory-copy records, and delete them in time to reduce memory consumption of the B-tree. We evaluate ICID in a system equipped with Intel Optane DC Persistent Memory Modules. For a typical KV store–LevelDB, our experimental results show that ICID achieves up to 16higher deduplication ratio and reduces the time cost of data deduplication by 47% on average compared with state-of-the-art deduplication schemes.

In data deduplication systems, chunking has a significant impact on the deduplication ratio and throughput. ExistingContent-Defined Chunking(CDC) approaches exploit a sliding window to calculate rolling hashes of the input data stream byte-by-byte, and then determine chunk cut-points if the rolling hash satisfies a given cut-condition. Since previous CDC approaches are extremely costly, it often significantly degrades the throughput of data deduplication systems. In this paper, we argue that calculating and checking the rolling hashes byte-by-byte is unnecessary. To reduce the CPU overhead of CDC, we propose ajump-based chunking(JC) approach. The key idea is to introduce a jump-condition, and the sliding window can jump over a specific length of the input data stream if the rolling hashes satisfy the jump-condition. Moreover, we also explore the impact of the cut-condition and the jump-condition on the chunk size. Our theoretic studies demonstrate the effectiveness and efficiency of JC, without compromising the deduplication ratio. Experimental results show that JC improves the throughput of chunking by about 2× on average compared with the state-of-the-art CDC approaches while still guaranteeing high deduplication ratio.