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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2014 Vol.4(4): 376-382 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V4.440

Enhanced Dynamic Whole File De-Duplication (DWFD) for Space Optimization in Private Cloud Storage Backup

M. Shyamala Devi, V. Vimal Khanna, and A. Naveen Bhalaji
Abstract—Cloud Storage provide users with abundant storage space and make user friendly for immediate data access. But there is a lack of analysis on optimizing cloud storage for effective data access. With the development of storage and technology, digital data has occupied more and more space. According to statistics, 60% of digital data is redundant, and the data compression can only eliminate intra-file redundancy. In order to solve these problems, De-Duplication has been proposed. Many organizations have set up private cloud storage with their unused resources for resource utilization. Since private cloud storage has limited amount of hardware resources, they need to optimally utilize the space to hold maximum data. In this paper, we discuss the flaws in existing methods for Data De-Duplication. Our proposed method namely Dynamic Whole File De-duplication (DWFD) provides dynamic space optimization in private cloud storage backup as well as increase the throughput and de-duplication efficiency.

Index Terms—Cloud backup, cloud computing, constant-size chunking, data de-duplication, full-file chunking, private storage cloud, redundancy.

The authors are with the R.M.D Engineering College, Chennai, India (e-mail: shyamalapmr@gmail.com, vimalkhanna93@gmail.com, anaveenbhalaji@gmail.com).

[PDF]

Cite: M. Shyamala Devi, V. Vimal Khanna, and A. Naveen Bhalaji, "Enhanced Dynamic Whole File De-Duplication (DWFD) for Space Optimization in Private Cloud Storage Backup," International Journal of Machine Learning and Computing vol.4, no. 4, pp. 376-382, 2014.

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