An efficient data segmentation and replication technique for cloud using fruit fly optimized centrality measures

Abstract

Cloud computing is a creating worldview to give dependable and versatile framework permitting the clients (data proprietors) to store their data and the data customers (clients) can get to the data from cloud servers. This paradigm decreases stockpiling and upkeep cost of the data proprietor. However, cloud data storage still gives rise to security related problems. In case of shared data, the data face both cloud-specific and insider threats. In this work, we propose FOA(fruit fly optimization algorithm) optimized centrality measure fragmentation and replication of information in the cloud for optimum performance and security that consider both security and performance issues. FOA is a technique for deducing global optimization based on the foraging character of the fruit fly. The sensory perception of the fruit fly is superior than that of other species, particularly the sense of smell and vision. In our methodology, we divide a data files and replicate the fragmented data over the cloud nodes using FOA centrality measures. Every one of the cloud node just store a single information data fragment that ensures even if there arise an occurrence of a successful attack,no important information is shown to the attacker. We also compare the performance of the our methodology with other standard replication schemes. Observed results shows higher level of security and performance imrpovements.