A multi‐feature‐based intelligent redundancy elimination scheme for cloud‐assisted health systems

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health systems. Deduplication eliminates the redundancy of duplicate blocks by storing one physical instance referenced multiple duplicates. Delta compression is usually regarded as a complementary technique deduplication further remove similar blocks, but our observations indicate that this disobedient when data have sparse blocks. In addition, there many overlapped deltas in resemblance detection process post-deduplication delta compression, which hinders efficiency and index phase inquires abundant non-similar resulting inefficient system throughput. Therefore, multi-feature-based scheme, called MFRE, proposed solve these problems. The similarity feature temporal locality excavated assist where well expresses attribute. Then, similarity-based dynamic locality-based discover more base minimise improve ratios. Moreover, clustering method based on block-relationship strategy bloom filters IO Experiments demonstrate method, compared state-of-the-art improves ratio throughput 9.68% 50%, respectively.