Secure Enterprise and Read Performance Enhancement in Data Deduplication for Secondary Storage
S. Usharani1, K. Kungumaraj2
1S. Usharani, Research Scholar, Department of Computer Science, Mother Teresa Women’s University, Kodaikanal (Tamil Nadu), India.
2K. Kungumaraj, Assistant Professor, PG, Department of Computer Science, Arulmigu Palaniandavar Arts College for Women, Palani (Tamil Nadu), India.
Manuscript received on 11 October 2019 | Revised Manuscript received on 20 October 2019 | Manuscript Published on 02 November 2019 | PP: 443-448 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10670982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1067.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the tremendous growth of available digital data, the use of Cloud Service Providers (CSPs) are gaining more popularity, since these types of services promise to provide convenient and efficient storage services to end-users by taking advantage of a new set of benefits and savings offered by cloud technologies in terms of computational, storage, bandwidth, and transmission costs. we propose solutions for different data types (text, image and video) for secure data de-duplication in cloud environments. Our schemes allow users to upload their data in a secure and efficient manner such that neither a semi-honest CSP nor a malicious user can access or compromise the security of the data. Moreover, we propose proof of storage protocols including Proof of Retrievability (POR) and Proof of Ownership (POW) so that users of cloud storage services are able to ensure that their data has been saved in the cloud without tampering or manipulation. Experimental results are provided to validate the effectiveness of the proposed schemes. proposes a method to improve the read performance by investigating the recently accessed chunks and their locality in the backup set (data stream). Based on this study of the distribution of chunks in the data stream, few chunks are identified that need to be accumulated and stored to serve the future read requests better. This identification and accumulation happen on cached chunks. By this a small degree of duplication of the de-duplicated data is introduced, but by later caching them together during the restore of the same data stream, the read performance is improved. Finally the read performance results obtained through experiments with trace datasets are presented and analyzed to evaluate the design.
Keywords: Cloud, Chunk Fragmentation, Significant, Recall, Queries.
Scope of the Article: Storage-Area Networks