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
- Charu Tyagi, Yougesh Kumar, Anju Panwar, Experimental Ascaridiasis Induced Immunosuppression in WLH Chicks: Biochemical Parameters , The Scientific Temper: Vol. 12 No. 1&2 (2021): The Scientific Temper
- Nisha Patil, Archana Bhise, Rajesh K. Tiwari, Fusion deep learning with pre-post harvest quality management of grapes within the realm of supply chain management , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Neeraj ., Anita Singhrova, Quantum Key Distribution-based Techniques in IoT , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Deepa Ramachandran VR VR, Kamalraj N, Hybrid deep segmentation architecture using dual attention U-Net and Mask-RCNN for accurate detection of pests, diseases, and weeds in crops , The Scientific Temper: Vol. 16 No. 07 (2025): The Scientific Temper
- V. Mahalakshmi, M. Manimekalai, Location Specific Paddy Yield Prediction using Monte Carlo Simulation incorporated Long Short-Term Memory , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Sanjeev Kumar, Saurabh Charaya, Rachna Mehta, Multi-Metric Evaluation Framework for Machine Learning-Based Load Prediction in e-Governance Systems , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- A. Jafar Ali, Dr.G. Ravi, D.I. George Amalarethinam, AI-Driven Swarm-Optimized Adaptive Routing Using Quantum-Inspired Neural Scheduling with Homomorphic Encryption , The Scientific Temper: Vol. 17 No. 02 (2026): The Scientific Temper
- Brijesh Singh, Ajay Massand, Determinants of Gen Z’s adoption of chatbots in online shopping: An empirical investigation , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- Santosh Kumar Sahu, B. R. Senthil kumar, Y. Aboobucker parvez, Ashish Verma, Assessment of noise levels by using noise prediction modeling , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Sharayu Mirasdar, Mangesh Bedekar, Knowledge graphs for NLP: A comprehensive analysis , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
<< < 11 12 13 14 15 16 17 18 19 20 > >>
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

