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A fast access big data approach for configurable and scalable object storage Enabling mixed fault-tolerance

dc.contributor.authorValêncio, Carlos Roberto [UNESP]
dc.contributor.authorCaetano, André Francisco Morielo [UNESP]
dc.contributor.authorColombini, Angelo Cesar
dc.contributor.authorTronco, Mário Luiz
dc.contributor.authorFortes, Márcio Zamboti
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Federal de São Carlos (UFSCar)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionFluminense Federal University (UFF)
dc.date.accessioned2018-12-11T17:13:31Z
dc.date.available2018-12-11T17:13:31Z
dc.date.issued2017-07-01
dc.description.abstractThe 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.en
dc.description.affiliationDepartment of Computer Science and Statistics - DCCE São Paulo State University (Unesp) Institute of Biosciences Humanities and Exact Sciences (Ibilce), Campus São José do Rio Preto
dc.description.affiliationDepartment of Computer Science and Statistics Federal University of São Carlos (UFSCar) São Carlos
dc.description.affiliationDepartment of Mechanical Engineering - EESC São Paulo University (USP) São Carlos
dc.description.affiliationDepartment of Electrical Engineering - TEE Fluminense Federal University (UFF)
dc.description.affiliationUnespDepartment of Computer Science and Statistics - DCCE São Paulo State University (Unesp) Institute of Biosciences Humanities and Exact Sciences (Ibilce), Campus São José do Rio Preto
dc.format.extent192-198
dc.identifierhttp://dx.doi.org/10.3844/jcssp.2017.192.198
dc.identifier.citationJournal of Computer Science, v. 13, n. 6, p. 192-198, 2017.
dc.identifier.doi10.3844/jcssp.2017.192.198
dc.identifier.file2-s2.0-85025129320.pdf
dc.identifier.issn1549-3636
dc.identifier.lattes4644812253875832
dc.identifier.orcid0000-0002-9325-3159
dc.identifier.scopus2-s2.0-85025129320
dc.identifier.urihttp://hdl.handle.net/11449/174933
dc.language.isoeng
dc.relation.ispartofJournal of Computer Science
dc.relation.ispartofsjr0,147
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectBig data
dc.subjectCache
dc.subjectData storage
dc.subjectErasure coding
dc.subjectObject storage
dc.titleA fast access big data approach for configurable and scalable object storage Enabling mixed fault-toleranceen
dc.typeArtigo
unesp.author.lattes4644812253875832[1]
unesp.author.orcid0000-0002-9325-3159[1]
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Biociências Letras e Ciências Exatas, São José do Rio Pretopt
unesp.departmentCiências da Computação e Estatística - IBILCEpt

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