Performance evaluation of RAM chunking algorithm in cloud backup service
Abstract
With the current explosion of digital data, the regulatory back-up data methods are the pending issue to be resolved. Deduplication is one of the main solutions to restrain the increase of duplicated copies of data and achieve cost-savings in data centers. Data deduplication is a technique for effectively reducing the storage requirement of practical backup by eliminating redundant data to ensure that only a single instance is stored in the storage medium. Chunking based deduplication is one of the most effective deduplication strategies, which replaces duplicated data with references to data already stored. In this thesis, we evaluate the performance of three different Content-based chunking algorithms (TTTD-S, AE, and RAM) by implementing them in C# in Visual Studio 2015 environment and then performing a series of systematic experiments. The results show superior performance of RAM in runtime and deduplication rate, while AE came second with a slight difference from RAM.
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