Optimal data storage and secured data retrieval technique in cloud environment

Abstract

Effective storage space utilization and secure data retrieval have emerged as crucial aspects of geographically distributed cloud storage. Building of distributed storage system with scalability, consistency, availability and security is a challenging task. In this context, this thesis aims at analyzing the significance of optimal data storage techniques in large scale data scenario, which enhances the performance of storage space utilization in an efficient and secured manner with decreased computation overhead and increased security. This thesis presents a suitable methodology for optimal data storage and secure data retrieval in a cloud scenario. To reduce the usage of storage space and computation overhead, numerous strategies have been included in this research. The effectiveness of the proposed work is evaluated using Industrial IoT dataset and the experimental results indicate that it outperforms current approaches. When investigating the application of an optimal data storage methodology, three perspectives are taken into consideration. In the first stage, time series-based deduplication is developed as a method of efficient utilization of storage space. The geographically distributed cloud receives data from wide range of sources for storage. Due to redundant data embedded in heterogeneous sources, huge amount of storage space is required and also involves additional computational cost. Thus, practicing of data deduplication is essential and is quite challenging. newline