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Entropy 2016, 18(12), 441; doi:10.3390/e18120441

Construction of Fractional Repetition Codes with Variable Parameters for Distributed Storage Systems

1,†
and
2,†,*
1
School of Electronics and Computer Engineering, Chonnam National University, Gwangju 61186, Korea
2
Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Korea
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 2 November 2016 / Revised: 1 December 2016 / Accepted: 2 December 2016 / Published: 8 December 2016
(This article belongs to the Section Information Theory)
View Full-Text   |   Download PDF [383 KB, uploaded 8 December 2016]   |  

Abstract

In this paper, we propose a new class of regular fractional repetition (FR) codes constructed from perfect difference families and quasi-perfect difference families to store big data in distributed storage systems. The main advantage of the proposed construction method is that it supports a wide range of code parameter values compared to existing ones, which is an important feature to be adopted in practical systems. When using one instance of the proposed codes for a given parameter set, we show that the amount of stored data is very close to that of an existing state-of-the-art optimal FR code. View Full-Text
Keywords: cloud storage; distributed storage systems; fractional repetition codes; FR capacity; perfect difference family; quasi-perfect difference family cloud storage; distributed storage systems; fractional repetition codes; FR capacity; perfect difference family; quasi-perfect difference family
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Park, H.; Kim, Y.-S. Construction of Fractional Repetition Codes with Variable Parameters for Distributed Storage Systems. Entropy 2016, 18, 441.

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