Redundancy Elimination Within Large Collections of Files

Ongoing advancements in technology lead to ever-increasing storage capacities. In spite of this, optimizing storage usage can still provide rich dividends. Several techniques based on delta-encoding and duplicate block suppression have been shown to reduce storage overheads, with varying requirements for resources such as computation and memory. We propose a new scheme for storage reduction that reduces data sizes with an effectiveness comparable to the more expensive techniques, but at a cost comparable to the faster but less effective ones. The scheme, called Redundancy Elimination at the Block Level (REBL), leverages the benefits of compression, duplicate block suppression, and delta-encoding to eliminate a broad spectrum of redundant data in a scalable and efficient manner. REBL generally encodes more compactly than compression (up to a factor of 14) and a combination of compression and duplicate suppression (up to a factor of 6.7). REBL also encodes similarly to a technique based on delta-encoding, reducing overall space significantly in one case. Furthermore, REBL uses super-fingerprints, a technique that reduces the data needed to identify similar blocks while dramatically reducing the computational requirements of matching the blocks: it turns O(n²) comparisons into hash table lookups. As a result, using super-fingerprints to avoid enumerating matching data objects decreases computation in the resemblance detection phase of REBL by up to a couple orders of magnitude.