• 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