Distributed deduplication with fingerprint index management model for big data storage in the cloud

As data progressively grows within data centers, the cloud storage models face several issues while storing data and offers abilities needed to shift data in an adequate time frame. This study aims to develop a distributed deduplication model to achieve scalable throughput and capacity utilizing many data servers for duplicating data in parallel with minimal loss. This paper proposes a new cloud storage model based on a distributed deduplication with the fingerprint index management (DDFI) model. The DDFI model operates on three main stages. At the initial stage, the DDFI model makes use of an effective routing technique depending upon the similarity level of the data, which leads to low network overhead by rapid identification of storage locations. In the second stage, the duplicate data identification procedure is carried out by the use of the MD5 algorithm. At the final stage, a fingerprint index management process is executed where a fingerprint index comprises fingerprints and its corresponding position details of every written chunk. For optimizing the results of the deduplication performance, the DDFI model manages the fingerprint index in storage space and only sometimes writes to disk at the same time as the cloud database scheme is idle. The simulation outcome exhibited that the presented DDFI model offered maximum results with a higher deduplication ratio (DR) with a minimum overhead of network bandwidth. From the detailed comparative analysis, it is inferred that the presented DFFI model offered maximum relative DR, maximum duplication performance, minimum read bandwidth, and write bandwidth.