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

被引:0
|
作者
Belu, Sabin [1 ]
Coltuc, Daniela [1 ]
机构
[1] Univ Politehn Bucuresti, Doctoral Sch Elect Telecommun & Informat Technol, Bucharest 061071, Romania
关键词
big data; automotive; delta encoding; bsdiff; vcdiff; OTA software update; Lempel-Ziv; LZ77; LZFG; LZMA; GENOME COMPRESSION;
D O I
10.3390/e24050574
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
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.
引用
收藏
页数:20
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