Nature - Inspired enhanced data deduplication for efficient cloud storage

Abstract

Cloud Computing is the delivery of computing as a service, which is specifically involved with Storage of data, enabling ubiquitous, convenient access to shared resources that are provided to computers and other devices as a utility over a network. Storage, which is considered to be the key attribute, is hindered by the presence of redundant copies of data. Data Deduplication is a specialized technique for data compression and duplicate detection for eliminating duplicate copies of data to make storage utilization efficient. Cloud Service Providers currently employ Hashing technique so as to avoid the presence of redundant copies. Apparently, there are a few major pitfalls which can be vanquished through the employment of a Nature - Inspired, Genetic Programming Approach, for deduplication. Genetic Programming is a systematic, domain - independent programming model making use of the ideologies of biological evolution so as to handle a complicated problem. A Sequence Matching Algorithm and Levenshtein's Algorithm are used for Text Comparison and then Genetic Programming concepts are utilized to detect the closest match. The performance of these three algorithms and hashing technique are compared. Since bio-inspired concepts, systems and algorithms are found to be more efficient, a Nature-Inspired Approach for data deduplication in cloud storage is implemented.