Data Deduplication with Random Substitutions

TL;DR

This paper analyzes the performance of data deduplication algorithms on data streams with probabilistic symbol substitutions, proposing modifications that achieve near-optimal performance and high compression ratios.

Contribution

It introduces an information-theoretic model for approximate deduplication with substitutions and proposes modifications to fixed-length schemes that perform near-optimally.

Findings

Fixed-length deduplication is unsuitable for substitution models.

Modified algorithms perform within a constant factor of optimal.

Variable-length deduplication achieves high compression ratios as entropy decreases.

Abstract

Data deduplication saves storage space by identifying and removing repeats in the data stream. Compared with traditional compression methods, data deduplication schemes are more time efficient and are thus widely used in large scale storage systems. In this paper, we provide an information-theoretic analysis on the performance of deduplication algorithms on data streams in which repeats are not exact. We introduce a source model in which probabilistic substitutions are considered. More precisely, each symbol in a repeated string is substituted with a given edit probability. Deduplication algorithms in both the fixed-length scheme and the variable-length scheme are studied. The fixed-length deduplication algorithm is shown to be unsuitable for the proposed source model as it does not take into account the edit probability. Two modifications are proposed and shown to have performances within a constant factor of optimal with the knowledge of source model parameters. We also study the conventional variable-length deduplication algorithm and show that as source entropy becomes smaller, the size of the compressed string vanishes relative to the length of the uncompressed string, leading to high compression ratios.