FEC-Seluge: Efficient, reliable, and secure large data dissemination using erasure codes

Hyun, Sangwon; Sun, Kun; Ning, Peng

doi:10.1016/j.comcom.2017.01.005

Cited by 2 publications

(1 citation statement)

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“…As these parameters (n, k) are configurable, an erasure coding algorithm could provide the same faulttolerance of replication methods, but instead providing much less storage overhead (Li and Li, 2013). As examples of method application we can cite the research of Hyun et al (2017;Al-Awami and Hossanein, 2016;Gribaudo et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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

Valêncio¹,

Caetano²,

Colombini³

et al. 2017

Journal of Computer Science

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Abstract: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.

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