Abstract—Cloud Storage provide users with abundant
storage space and make user friendly for immediate acquiring
of data, which is the foundation of all kinds of cloud
applications. However, there is a lack of deep studies on how to
optimize cloud storage aiming at improvement of data access
performance. With the development of storage and computer
technology, digital data has occupied more and more space.
According to statistics, 60% of these digital data is redundant,
and the traditional data compression can only eliminate the
intra-file redundancy. The growth in redundant data will
continue, unabated. The issue is how to manage this
phenomenon, while operating with the assumption that the
growth will likely accelerate. In order to solve these problems,
Data De-Duplication has been proposed. Many organizations
have set up private clouds for best resource utilization. An
organization can built private cloud storage with their unused
resources for storing their personal data. Since private cloud
storage has a limited amount of hardware resources, they need
to optimally utilize the space to accommodate maximum data.
Data De-Duplication is an effective technique to optimize the
utilization of storage space backup by avoiding the redundancy.
In this paper, we are going to discuss the flaws in the existing
de-duplication methods and introduce new methods for Data
De-Duplication. Our proposed method namely Intensive
Indexing (I2D) De-duplication which is the enhanced File level
de-duplication that 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.
M. Shyamala Devi and Steven S. Fernandez are with R.M.D Engineering
College, Chennai, India (e-mail: shyamalapmr@gmail.com).
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Cite: M. Shyamala Devi and Steven S. Fernandez, "Enhanced Intensive Indexing (I2D) De-Duplication for Space Optimization in Private Cloud Storage Backup," International Journal of Computer Theory and Engineering vol. 7, no. 2, pp. 113-119, 2015.
