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In this paper, we study the problem of storing an archive of versioned datain a reliable and efficient manner in distributed storage systems. We propose anew storage technique called differential erasure coding (DEC) where thedifferences (deltas) between subsequent versions are stored rather than thewhole objects, akin to a typical delta encoding technique. However, unlikedelta encoding techniques, DEC opportunistically exploits the sparsity (i.e.,when the differences between two successive versions have few non-zero entries)in the updates to store the deltas using compressed sensing techniques appliedwith erasure coding. We first show that DEC provides significant savings in thestorage size for versioned data whenever the update patterns are characterizedby in-place alterations. Subsequently, we propose a practical DEC framework soas to reap storage size benefits against not just in-place alterations but alsoreal-world update patterns such as insertions and deletions that alter theoverall data sizes. We conduct experiments with several synthetic workloads todemonstrate that the practical variant of DEC provides significant reductionsin storage overhead (up to 60\% depending on the workload) compared to baselinestorage system which incorporates concepts from Rsync, a delta encodingtechnique to store and synchronize data across a network.

Compressed Differential Erasure Codes for Efficient Archival of Versioned Data