Chunk Content is not Enough: Chunk-Context Aware Resemblance Detection for Deduplication Delta Compression

TL;DR

This paper introduces CARD, a chunk-context aware resemblance detection algorithm that combines internal chunk structure and context information, significantly improving redundancy detection and detection speed in cloud storage deduplication.

Contribution

The paper proposes a novel neural network-based method that enhances resemblance detection by integrating chunk content and context, outperforming existing methods in accuracy and efficiency.

Findings

Detects up to 75.03% more redundant data

Accelerates resemblance detection by 5.6 to 17.8 times

Effectively reduces impact of small data changes

Abstract

With the growing popularity of cloud storage, removing duplicated data across users is getting more critical for service providers to reduce costs. Recently, Data resemblance detection is a novel technology to detect redundancy among similarity. It extracts feature from each chunk content and treat chunks with high similarity as candidates for removing redundancy. However, popular resemblance methods such as "N-transform" and "Finesse" use only the chunk data for feature extraction. A minor modification on the data chunk could seriously deteriorate its capability for resemblance detection. In this paper, we proposes a novel chunk-context aware resemblance detection algorithm, called CARD, to mitigate this issue. CARD introduces a BP-Neural network-based chunk-context aware model, and uses N-sub-chunk shingles-based initial feature extraction strategy. It effectively integrates each data chunk content's internal structure with the context information for feature extraction, the impact of small changes in data chunks is significantly reduced. To evaluate its performance, we implement a CARD prototype and conduct extensive experiments using real-world data sets. The results show that CARD can detect up to 75.03% more redundant data and accelerate the resemblance detection operations by 5.6 to 17.8 times faster compared with the state-of-the-art resemblance detection approaches.