Chunk2vec: A novel resemblance detection scheme based on Sentence‐BERT for post‐deduplication delta compression in network transmission

Author:

Wang Chunzhi1,Wang Keguan1ORCID,Li Min2,Wei Feifei3,Xiong Neal4ORCID

Affiliation:

1. School of Computer Science Hubei University of Technology Wuhan China

2. School of Artificial Intelligence Hubei Business College Wuhan China

3. School of Information Management Hubei University of Economics Wuhan China

4. Department of Mathematics and Computer Science Northeastern State University Tahlequah Oklahoma USA

Abstract

AbstractDelta compression, as a complementary technique for data deduplication, has gained widespread attention in network storage systems. It can eliminate redundant data between non‐duplicate but similar chunks that cannot be identified by data deduplication. The network transmission overhead between servers and clients can be greatly reduced by using data deduplication and delta compression techniques. Resemblance detection is a technique that identifies similar chunks for post‐deduplication delta compression in network storage systems. The existing resemblance detection approaches fail to detect similar chunks with arbitrary similarity by setting a similarity threshold, which can be suboptimal. In this paper, the authors propose Chunk2vec, a resemblance detection scheme for delta compression that utilizes deep learning techniques and Approximate Nearest Neighbour Search technique to detect similar chunks with any given similarity range. Chunk2vec uses a deep neural network, Sentence‐BERT, to extract an approximate feature vector for each chunk while preserving its similarity with other chunks. The experimental results on five real‐world datasets indicate that Chunk2vec improves the accuracy of resemblance detection for delta compression and achieves higher compression ratio than the state‐of‐the‐art resemblance detection technique.

Publisher

Institution of Engineering and Technology (IET)

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