Enhancing Deduplication Efficiency Using Triple Bytes Cutters and Multi Hash Function

Abstract

Managing big data backups is challenging due to high volumes of redundant data. Data deduplication is widely used but incurs significant computational and time costs. This paper proposes a hybrid deduplication system that combines file-level and block-level methods to enhance deduplication while reducing costs. File-level deduplication eliminates duplicate files, while block-level deduplication is applied to non-duplicated files using a dynamic list of divisors to enhance deduplication. A multi-hash function generates three hash values for each file or chunk to improve chunking speed and reduce hash collisions. The proposed hybrid system outperforms other state-ofthe-art methods in terms of time, data deduplication ratio, and deduplication gain. Experimental results show reductions of 97.2%, 91.6%, and 82.1% in data size for Dataset 1, Dataset 2, and Dataset 3, respectively, and demonstrate that the proposed multi-hash function is faster and requires less storage than other hash functions.