Understanding Differencing Algorithms for Mobile Application Updates

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Abstract
Mobile application updates occur frequently, and they continue to add considerable traffic over the Internet. Differencing algorithms, which compute a small delta between the new version and the old version, are often employed to reduce the update overhead. Researchers have proposed many differencing algorithms over the years. Unfortunately, it is currently unknown how these algorithms quantitatively perform for different categories of applications. It is also challenging to know the impacts of different techniques and whether a technique in one algorithm can be integrated into another algorithm for further performance improvement. This paper conducts the first systematic study to understand the performance of four widely used differencing algorithms for mobile application updates, including xdelta3, bsdiff, archive-patcher, and HDiffPatch with respect to five key metrics, including compression ratio, differencing time/memory overhead, and reconstruction time/memory overhead. We perform measurements for 200 mobile applications, and analyze key techniques (such as decompressing-before-differencing, sliding window, and copy instructions merging) that influence the performance of these algorithms. We have provided four important findings which give insights to further optimize for performance improvement. Guided by these insights, we have also proposed a novel algorithm, sdiff , which achieves the smallest compression ratio to state-of-the-art algorithms by combining an appropriately chosen set of key techniques.

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