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Adaptive multi-tier intelligent data manager for Exascale

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dc.affiliation.dptoUC3M. Departamento de Informática
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Arquitectura de Computadores, Comunicaciones y Sistemas
dc.contributor.authorCarretero Pérez, Jesús
dc.contributor.authorGarcía Blas, Francisco Javier
dc.contributor.authorAldinucci, Marco
dc.contributor.authorBesnard, Jean Baptiste
dc.contributor.authorAcquaviva, Jean Thomas
dc.contributor.authorBrinkmann, Andre
dc.contributor.authorVef, Marc Andre
dc.contributor.authorJeannot, Emmanuel
dc.contributor.authorMiranda, Alberto
dc.contributor.authorNou, Ramon
dc.contributor.authorRiedel, Morris
dc.contributor.authorTorquati, Massimo
dc.contributor.authorWolf, Felix
dc.contributor.funderEuropean Commission
dc.contributor.funderMinisterio de Ciencia e Innovación (España)
dc.date.accessioned2024-06-07T09:00:18Z
dc.date.available2024-06-07T09:00:18Z
dc.date.issued2023-08-04
dc.description.abstractThe main objective of the ADMIRE project1 is the creation of an active I/O stack that dynamically adjusts computation and storage requirements through intelligent global coordination, the elasticity of computation and I/O, and the scheduling of storage resources along all levels of the storage hierarchy, while offering quality-of-service (QoS), energy efficiency, and resilience for accessing extremely large data sets in very heterogeneous computing and storage environments. We have developed a framework prototype that is able to dynamically adjust computation and storage requirements through intelligent global coordination, separated control, and data paths, the malleability of computation and I/O, the scheduling of storage resources along all levels of the storage hierarchy, and scalable monitoring techniques. The leading idea in ADMIRE is to co-design applications with ad-hoc storage systems that can be deployed with the application and adapt their computing and I/O behaviour on runtime, using malleability techniques, to increase the performance of applications and the throughput of the applications.
dc.description.sponsorshipEuroHPC project "Adaptivemulti-tier intelligent data manager for Exascale" under grant 956748—ADMIRE—H2020-JTI-EuroHPC-2019-1
dc.description.sponsorshipAgencia Española de Investigación under GrantPCI2021-121966
dc.identifier.bibliographicCitationJesus Carretero, Javier Garcia-Blas, Marco Aldinucci, Jean Baptiste Besnard, Jean-Thomas Acquaviva, André Brinkmann, Marc-André Vef, Emmanuel Jeannot, Alberto Miranda, Ramon Nou, Morris Riedel, Massimo Torquati, and Felix Wolf. 2023. Adaptive multi-tier intelligent data manager for Exascale. In Proceedings of the 20th ACM International Conference on Computing Frontiers (CF '23). Association for Computing Machinery, New York, NY, USA, 285–290. https://doi.org/10.1145/3587135.3592174
dc.identifier.doihttps://doi.org/10.1145/3587135.3592174
dc.identifier.isbn979-8-4007-0140-5
dc.identifier.publicationfirstpage285
dc.identifier.publicationlastpage290
dc.identifier.urihttps://hdl.handle.net/10016/43950
dc.identifier.uxxiCC/0000034552
dc.language.isoeng
dc.publisherACM DL
dc.relation.eventdateBologna (Italia)
dc.relation.eventplace9-11 mayo 2023
dc.relation.eventtitle20th ACM International Conference on Computing Frontiers
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/956748/ADMIRE
dc.relation.projectIDGobierno de España. PCI2021-121966
dc.rights© 2023 Copyright held by the owner/author(s
dc.rights.accessRightsopen access
dc.subject.ecienciaInformática
dc.subject.otherhigh-performance i/o
dc.subject.othermalleability
dc.subject.othermonitoring
dc.subject.otherpredictive models
dc.subject.otherscheduling algorithms
dc.titleAdaptive multi-tier intelligent data manager for Exascale
dc.typeconference paper
dc.type.hasVersionAM
dspace.entity.typePublication
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