BTEDD: Block-level tokens for efficient data deduplication in public cloud infrastructures
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.3.16Keywords:
Cloud storage, Data Deduplication Techniques, Block-level deduplication, Cloud Data Security, Data Storage ManagementDimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In today's digital era, the exponential growth of data necessitates effective storage and management solutions. The cloud has vast storage possibilities to store huge amounts of data. Public access to the cloud leads to duplicate copies of data stored in the storage. Maintaining a single copy of data in the cloud is most important for efficient data storage management. This paper introduces a groundbreaking strategy for improving the efficacy of cloud storage through innovative data deduplication techniques at the block levels. The block-level duplication verification efficiently identifies the duplicate data in the storage. It helps to protect the duplicate storage in the cloud data storage infrastructure. The block-level deduplication technique uses variable-length blocks based on the duplicate content of the block. Initially, A file is divided into a number of blocks with a size of 5kb. According to the proposed method, If any block is partially matched with a block already stored in the cloud, then that block is further divided into smaller blocks based on the matching percentage. The smaller blocks help to deduplicate the data more effectively. The work is implemented in a live cloud setting with a C# application hosted on MyASP.NET. The proposed methodology's effectiveness is validated against existing deduplication techniques. The results reveal a marked improvement in storage utilization and data management, affirming the potential of the approach to revolutionize cloud storage efficiency.Abstract
How to Cite
Downloads
Similar Articles
- Shripada Patil, Sandeep N. Jagdale, Prashant Kalshetti, Management education system in the 21st century: Challenges and opportunities , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Aditi Malik, Rishi Chaudhry, Mohit, Urvashi Suryavanshi, Mapping the landscape of political advertising research: A comprehensive bibliometric analysis , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- K. Vani, S. Sujatha, Fault tolerance systems in open source cloud computing environments–A systematic review , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- D. Jayadurga, A. Chandrabose, Expanding the quantity of virtual machines utilized within an open-source cloud infrastructure , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- S ChandraPrabha, S. Kantha Lakshmi, P. Sivaraaj, Data analysis and machine learning-based modeling for real-time production , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- ABHAYA K. SINGH, IMPLICATIONS OF CLIMATE CHANGE IN THE HIMALAYAN REGION AND ITS IMPACT ON INDIAN SECURITY , The Scientific Temper: Vol. 2 No. 1&2 (2011): The Scientific Temper
- A. Anand, A. Nisha Jebaseeli, AI-driven real-time performance optimization and comparison of virtual machines and containers in cloud environments , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Isreal Zewide, Wondwosen Wondimu, Melash Woldu, Kibnesh Admasu, Maize (Zea mays L.) Productivity as affected by different ratios of fertilizer (blended NPS) and inter row spacing at West Omo, South-West Ethiopia , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
- Chandrasekaran M, Rajesh P K, Optimization of cost to customer of power train in commercial vehicle using knapsack dynamic programming influenced by vehicle IoT data , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- S. Prabagar, Vinay K. Nassa, Senthil V. M, Shilpa Abhang, Pravin P. Adivarekar, Sridevi R, Python-based social science applications’ profiling and optimization on HPC systems using task and data parallelism , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
Most read articles by the same author(s)
- Sabeerath K, Manikandasaran S. Sundaram, ESPoW: Efficient and secured proof of ownership method to enable authentic deduplicated data access in public cloud storage , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper