Harnessing metadata characteristics for efficient deduplication in distributed storage Systems

Abstract

As storage capacity requirements grow, storage systems are becoming distributed, and that distribution poses a challenge for space savings processes. In this thesis, I design and implement a mechanism for storing only a single instance of duplicated data within a distributed storage system which selectively performs deduplication across each of the independent computers, known as nodes, used for storage. This involves analyzing the contents of each node for objects with characteristics more likely to have duplicates elsewhere, particularly using duplication within a node as the indicative property - an object duplicated many times in a dataset will likely be duplicated at least once in some node. An inter-node system is responsible for efficiently collecting and distributing the information of these potential duplicates. A test implementation was built and run on several data sets characteristic of common storage workloads where deduplication is important, while distributing across 128 nodes. The efficiency of this implementation was analyzed and compared against the savings when distributed across 128 nodes with deduplication performed only between duplicate objects within each node, without inter-node deduplication. The inter-node deduplication was found to increase the space saved by a factor of four to six times. This represented in one case, a file storage server, only a quarter of potential savings due to the majority of potential savings being in files with only a few duplicates. This left a low probability of locating duplication within a single node. However in another case, a collection of over 100 virtual machine images, nearly all potential duplicates were found due to the high number of duplicates for each object, providing an example of where this inter-node mechanism can be most useful.