Data deduplication has been broadly used in the Cloud due to its storage space saving ability. One issue with deduplication is a phenomenon called data fragmentation. To deal with data fragmentation, Cloud implements a procedure that diminishes the restore performance. Although capping methods have been developed to alleviate data fragmentation, they employ rewriting procedures that are only partially successful. To address this problem, TSYS School assistant professor of computer science Yi Zhou and his research colleagues from Jinan University (China) and the University of Exeter (United Kingdom) propose a multi-segment greedy rewriting method named MGRM. As they explain in their study forthcoming in IEEE Transactions on Cloud Computing , MGRM works sequentially to rewrite in a way that achieves a good balance between deduplication and restore performance. To do so, it adaptively switches between two working modes – an optimal rewriting mode ...
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