DocumentCode :
659510
Title :
Construction of exact-BASIC codes for distributed storage systems at the MSR point
Author :
Hanxu Hou ; Shum, Kenneth W. ; Hui Li
Author_Institution :
Shenzhen Grad. Sch., Shenzhen Key Lab. of Cloud Comput. Tech. & App., Peking Univ., Shenzhen, China
fYear :
2013
fDate :
6-9 Oct. 2013
Firstpage :
33
Lastpage :
38
Abstract :
Regenerating codes (RGC) are a class of distributed storage codes that can provide efficient repair of failure nodes in distributed storage systems. In general, the reduction of repair bandwidth of RGC is at the expense of a small increase in storage cost and computational cost. The high computational complexity of data coding over a finite field of large size makes it unsuitable for practical distributed storage systems. BASIC codes, which stands for Binary Addition and Shift Implementable Convolutional codes, is introduced in [1] with the aim of reducing computational complexity, while retaining the benefits of RGC. In this paper, we present a construction of exact-repair BASIC codes at the minimum-storage point (MSR). A helper node needs no coding to repair a failure node for the minimum-storage BASIC codes. The results of simulation show minimum-storage BASIC codes outperform Cauchy Reed-Solomon codes in both repairing cost and coding cost.
Keywords :
computational complexity; convolutional codes; distributed processing; storage management; MSR point; RGC; binary addition; coding cost; computational complexity; computational cost; data coding; distributed storage codes; distributed storage systems; exact-BASIC codes; exact-repair BASIC codes; failure node repairing; helper node; minimum-storage point; regenerating codes; repair bandwidth reduction; repairing cost; shift implementable convolutional codes; storage cost; Bandwidth; Computational complexity; Decoding; Educational institutions; Encoding; Maintenance engineering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data, 2013 IEEE International Conference on
Conference_Location :
Silicon Valley, CA
Type :
conf
DOI :
10.1109/BigData.2013.6691659
Filename :
6691659
Link To Document :
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