Research Article Open Access

A Fast Access Big Data Approach for Configurable and Scalable Object Storage Enabling Mixed Fault-Tolerance

Carlos Roberto Valêncio1, André Francisco Morielo Caetano1, Angelo Cesar Colombini2, Mário Luiz Tronco3 and Márcio Zamboti Fortes4
  • 1 São Paulo State University (Unesp), Brazil
  • 2 Federal University of São Carlos (UFSCar) São Carlos, Brazil
  • 3 São Paulo University (USP) São Carlos, Brazil
  • 4 Fluminense Federal University (UFF), Brazil


The progressive growth in the volume of digital data has become a technological challenge of great interest in the field of computer science. That comes because, with the spread of personal computers and networks worldwide, content generation is taking larger proportions and very different formats from what had been usual until then. To analyze and extract relevant knowledge from these masses of complex and large volume data is particularly interesting, but before that, it is necessary to develop techniques to encourage their resilient storage. Very often, storage systems use a replication scheme for preserving the integrity of stored data. This involves generating copies of all information that, if lost by individual hardware failures inherent in any massive storage infrastructure, do not compromise access to what was stored. However, it was realized that accommodate such copies requires a real storage space often much greater than the information would originally occupy. Because of that, there is error correction codes, or erasure codes, which has been used with a mathematical approach considerably more refined than the simple replication, generating a smaller storage overhead than their predecessors techniques. The contribution of this work is a fully decentralized storage strategy that, on average, presents performance improvements of over 80% in access latency for both replicated and encoded data, while minimizing by 55% the overhead for a terabyte-sized dataset when encoded and compared to related works of the literature.

Journal of Computer Science
Volume 13 No. 6, 2017, 192-198


Submitted On: 23 April 2017 Published On: 1 July 2017

How to Cite: Valêncio, C. R., Caetano, A. F. M., Colombini, A. C., Tronco, M. L. & Fortes, M. Z. (2017). A Fast Access Big Data Approach for Configurable and Scalable Object Storage Enabling Mixed Fault-Tolerance. Journal of Computer Science, 13(6), 192-198.

  • 0 Citations



  • Erasure Coding
  • Data Storage
  • Cache
  • Object Storage
  • Big Data