A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry

Entropy (Basel). 2022 Apr 19;24(5):574. doi: 10.3390/e24050574.

Abstract

We propose an innovative delta-differencing algorithm that combines software-updating methods with LZ77 data compression. This software-updating method relates to server-side software that creates binary delta files and to client-side software that performs software-update installations. The proposed algorithm creates binary-differencing streams already compressed from an initial phase. We present a software-updating method suitable for OTA software updates and the method's basic strategies to achieve a better performance in terms of speed, compression ratio or a combination of both. A comparison with publicly available solutions is provided. Our test results show our method, Keops, can outperform an LZMA (Lempel-Ziv-Markov chain-algorithm) based binary differencing solution in terms of compression ratio in two cases by more than 3% while being two to five times faster in decompression. We also prove experimentally that the difference between Keops and other competing delta-creator software increases when larger history buffers are used. In one case, we achieve a three times better performance for a delta rate compared to other competing delta rates.

Keywords: LZ77; LZFG; LZMA; Lempel–Ziv; OTA software update; automotive; big data; bsdiff; delta encoding; vcdiff.