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dc.contributor.authorHajnal, Akos-
dc.contributor.authorNagy, Eniko-
dc.contributor.authorKacsuk, Peter-
dc.contributor.authorMarton, Istvan-
dc.date.accessioned2023-04-28T17:16:52Z-
dc.date.available2023-04-28T17:16:52Z-
dc.date.issued2018-07-
dc.identifier.issn2616-6127-
dc.identifier.issn2617-4383-
dc.identifier.otherhttps://doi.org/10.32010/26166127.2018.1.1.66.86-
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/10-
dc.description.abstractTransferring large data files between various storages including cloud storages is an important task both for academic and commercial users. This should be done in an efficient and secure way. The paper describes Data Avenue that fulfills all these conditions. Data Avenue can efficiently transfer large files even in the range of TerraBytes among storages having very different access protocols (Amazon S3, OpenStack Swift, SFTP, SRM, iRODS, etc.). It can be used in personal, organizational and public deployment with all the security mechanisms required for these usage configurations. Data Avenue can be used by a GUI as well as by a REST API. The papers describes in detail all these features and usage modes of Data Avenue and also provides performance measurement results proving the efficiency of the tool that can be accessed and used via several public web pages.en_US
dc.language.isoenen_US
dc.publisherAzerbaijan Journal of High Performance Computingen_US
dc.subjectData managementen_US
dc.subjectData transferen_US
dc.subjectData migrationen_US
dc.subjectCloud storageen_US
dc.titleDATA MIGRATION FOR LARGE SCIENTIFIC DATASETS IN CLOUDSen_US
dc.typeArticleen_US
dc.source.journaltitleAzerbaijan Journal of High Performance Computingen_US
dc.source.volume1en_US
dc.source.issue1en_US
dc.source.beginpage66en_US
dc.source.endpage86en_US
dc.source.numberofpages21en_US
Appears in Collections:Azerbaijan Journal of High Performance Computing

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