Hash-Indexing Block-Based Deduplication Algorithm for Reducing Storage in the Cloud

Cloud storage is essential for managing user data to store and retrieve from the distributed data centre. The storage service is distributed as pay a service for accessing the size to collect the data. Due to the massive amount of data stored in the data centre containing similar information and file structures remaining in multi-copy, duplication leads to increase storage space. The potential deduplication system doesn’t make efficient data reduction because of inaccuracy in finding similar data analysis. It creates a complex nature to increase the storage consumption under cost. To resolve this problem, this paper proposes an efficient storage reduction called Hash-Indexing Block-based Deduplication (HIBD) based on Segmented Bind Linkage (SBL) Methods for reducing storage in a cloud environment. Initially, preprocessing is done using the sparse augmentation technique. Further, the preprocessed files are segmented into blocks to make Hash-Index. The block of the contents is compared with other files through Semantic Content Source Deduplication (SCSD), which identifies the similar content presence between the file. Based on the content presence count, the Distance Vector Weightage Correlation (DVWC) estimates the document similarity weight, and related files are grouped into a cluster. Finally, the segmented bind linkage compares the document to find duplicate content in the cluster using similarity weight based on the coefficient match case. This implementation helps identify the data redundancy efficiently and reduces the service cost in distributed cloud storage.