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.
Highlights
As users produce a tremendous amount of data everyday and these big data need to be stored in cloud storage [1,2], the efficiency and reliability of distributed storage systems (DSSs) become more crucial
There exists a trade-off between storage and repair bandwidth for general regenerating codes (RCs) and its two extreme points correspond to the minimum storage regenerating (MSR) codes and the minimum bandwidth regenerating (MBR)
We propose a construction method of regular fractional repetition (FR) codes based on (Q)perfect difference family (PDF)
Summary
As users produce a tremendous amount of data everyday and these big data need to be stored in cloud storage [1,2], the efficiency and reliability of distributed storage systems (DSSs) become more crucial. The proposed codes can support a wide range of code parameter values, especially, any integer-valued code length larger than a certain value This property is valuable for practical DSSs where each data has an arbitrary size and different importance, so it may need to be repeated a different number of times or distributed along a different number of nodes. The proposed FR codes are compared with existing regular FR codes in terms of the constructible code parameters and the actual size of stored data for each number of connected nodes. This comparison shows that the amount of stored data based on one instance of proposed codes is not far from the FR capacity bound [14] and very close to that of an existing state-of-the-art optimal FR code under the same parameter values
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