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A data preparation approach for cloud storage based on containerized parallel patterns

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemases
dc.contributor.authorCarrizales, Diana
dc.contributor.authorSánchez Gallegos, Dante D.
dc.contributor.authorReyes, Hugo
dc.contributor.authorGonzález Compean, J.L.
dc.contributor.authorMorales Sandoval, Miguel
dc.contributor.authorCarretero Pérez, Jesús
dc.contributor.authorGalaviz Mosqueda, Alejandro
dc.date.accessioned2021-12-17T12:24:01Z
dc.date.available2021-12-17T12:24:01Z
dc.date.issued2019-10-10
dc.description.abstractIn this paper, we present the design, implementation, and evaluation of an efficient data preparation and retrieval approach for cloud storage. The approach includes a deduplication subsystem that indexes the hash of each content to identify duplicated data. As a consequence, avoiding duplicated content reduces reprocessing time during uploads and other costs related to outsource data management tasks. Our proposed data preparation scheme enables organizations to add properties such as security, reliability, and cost-efficiency to their contents before sending them to the cloud. It also creates recovery schemes for organizations to share preprocessed contents with partners and end-users. The approach also includes an engine that encapsulates preprocessing applications into virtual containers (VCs) to create parallel patterns that improve the efficiency of data preparation retrieval process. In a study case, real repositories of satellite images, and organizational files were prepared to be migrated to the cloud by using processes such as compression, encryption, encoding for fault tolerance, and access control. The experimental evaluation revealed the feasibility of using a data preparation approach for organizations to mitigate risks that still could arise in the cloud. It also revealed the efficiency of the deduplication process to reduce data preparation tasks and the efficacy of parallel patterns to improve the end-user service experience.en
dc.description.sponsorshipThis research was supported by "Fondo Sectorial de Investigación para la Educación";, SEP-CONACyT Mexico, through projects 281565 and 285276.en
dc.identifier.bibliographicCitationCarrizales D. et al. (2019) A Data Preparation Approach for Cloud Storage Based on Containerized Parallel Patterns. In: Montella R., Ciaramella A., Fortino G., Guerrieri A., Liotta A. (eds) Internet and Distributed Computing Systems. IDCS 2019. Lecture Notes in Computer Science, vol 11874. Springer, Cham. https://doi.org/10.1007/978-3-030-34914-1_45en
dc.identifier.doihttps://doi.org/10.1007/978-3-030-34914-1_45
dc.identifier.isbn978-3-030-34913-4
dc.identifier.publicationfirstpage478
dc.identifier.publicationlastpage490
dc.identifier.publicationtitleInternet and Distributed Computing Systems: 12th International Conference, IDCS 2019. Naples, Italy, October 10&-12, 2019. Proceedings
dc.identifier.urihttps://hdl.handle.net/10016/33790
dc.identifier.uxxiCC/0000032837
dc.language.isoeng
dc.publisherSpringeren
dc.relation.eventdate2019-10-10
dc.relation.eventplaceITALIAes
dc.relation.eventtitleInternet and Distributed Computing Systems (12th International Conference, IDCS 2019)en
dc.rights© Springer Nature Switzerland AG 2019en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaInformáticaes
dc.subject.otherdeduplication systemsen
dc.subject.othervirtual containersen
dc.subject.otherparallel patternsen
dc.subject.othercontent deliveryen
dc.subject.othercloud storageen
dc.titleA data preparation approach for cloud storage based on containerized parallel patternsen
dc.typeconference proceedings*
dc.type.hasVersionAM*
dspace.entity.typePublication
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