• DocumentCode
    659509
  • Title

    Efficient updates in cross-object erasure-coded storage systems

  • Author

    Esmaili, Kyumars Sheykh ; Chiniah, Aatish ; Datta, Amitava

  • Author_Institution
    Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
  • fYear
    2013
  • fDate
    6-9 Oct. 2013
  • Firstpage
    28
  • Lastpage
    32
  • Abstract
    In the past few years erasure codes have been increasingly embraced by distributed storage systems as an alternative for replication, since they provide high fault-tolerance for low overheads. Erasure codes, however, have few shortcomings that need to be addressed to make them a complete solution for networked storage systems. Lack of support for efficient data repair and data update are the two most notable shortcomings. We recently proposed to use a 2-dimensional product code-Reed-Solomon coding per object and simple XORing across objects- and showed that at a reasonable storage overhead, it can greatly reduce the repair cost. In this paper we propose an efficient approach to handle data updates in cross-object erasure-coded storage systems. Our proposed solution has been implemented and experimentally evaluated. Our results show that compared to the naive approach (re-encoding the data), our proposed scheme can considerably decrease the update cost, especially for when the number of updated blocks is small.
  • Keywords
    Reed-Solomon codes; distributed processing; fault tolerance; product codes; storage management; 2-dimensional product code-Reed-Solomon coding; XORing; cross-object erasure-coded storage system; data repair; data update; distributed storage systems; fault-tolerance; networked storage systems; Cost function; Educational institutions; Encoding; Generators; Maintenance engineering; Polynomials; Reed-Solomon codes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data, 2013 IEEE International Conference on
  • Conference_Location
    Silicon Valley, CA
  • Type

    conf

  • DOI
    10.1109/BigData.2013.6691658
  • Filename
    6691658