Issue |
ITM Web Conf.
Volume 73, 2025
International Workshop on Advanced Applications of Deep Learning in Image Processing (IWADI 2024)
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Article Number | 03021 | |
Number of page(s) | 9 | |
Section | Blockchain, AI, and Technology Integration | |
DOI | https://doi.org/10.1051/itmconf/20257303021 | |
Published online | 17 February 2025 |
Comparative Analysis of SEC DED, EVEN-ODD, and Pyramid Codes for Distributed Storage Systems: A MATLAB-Based Study
College of Electronics and Information Engineering, Shenzhen University, 518060 Shenzhen, China
* Corresponding author: xuqinju@ldy.edu.rs
This study presents a comparative analysis of three erasure coding schemes—Single Error Correction Double Error Detection (SEC DED), EVEN-ODD, and Pyramid codes—within distributed storage systems, utilizing MATLAB for simulation. The research focuses on key metrics such as storage overhead, recovery time, and fault tolerance to elucidate the performance characteristics and practical applicability of each scheme. Single Error Correction Double Error Detection coding balances recovery speed and fault tolerance but incurs higher storage overhead due to its double-parity structure. EVEN-ODD coding, renowned for its storage efficiency and rapid recovery capabilities, exhibits limitations in fault tolerance. Conversely, Pyramid codes offer robust multidimensional error correction, making them suitable for large-scale storage environments, albeit at the cost of increased storage and extended recovery times. By examining these schemes under various scenarios, the analysis highlights their respective trade-offs and provides insights for selecting the optimal coding scheme based on specific system requirements. The study concludes with recommendations for enhancing erasure coding effectiveness in distributed environments and suggests areas for future research, including hybrid coding schemes and machine learning optimizations.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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