Open Access

Institute of Electrical and Electronics Engineers (IEEE)

IEEE Access

, volume 8 , pages 18681-18692

A Cloud-Based Framework for Machine Learning Workloads and Applications

Álvaro Bernalte-García ¹

Jesús Marco de Lucas ¹

M. Darryl Antonacci ²

Wolfgang zu Castell ^{3, 4}

Mario Alejandro David ⁵

Marcus Hardt ⁶

Lara Lloret ¹

Germán Moltó ⁷

Marcin Plociennik ⁸

Viet Tran ⁹

Andy S. Alic ⁷

Miguel Caballer ⁷

I. Campos ¹

Alessandro Costantini ¹⁰

Stefan Dlugolinsky ⁹

Doina Cristina Duma ¹⁰

Giacinto Donvito ²

Jorge Gomes ⁵

Ignacio Heredia ¹

Keiichi Ito ³

Valentin M. Kozlov ⁶

Giang Trong Nguyen ⁹

Pablo Orviz Fernández ¹

Zdeněk Šustr ¹¹

Pawel Wolniewicz ⁸

Hide authors affiliations Show authors affiliations: 11 affiliations

IFCA (CSIC-UC), Santander, Spain |

INFN Bari, Bari, Italy |

Helmholtz Zentrum München, Deutsches Forschungszentrum für Gesundheit, Oberschleißheim, Germany |

⁴

Department of Mathematics, Technische Universität München, Munich, Germany |

⁵

Laboratory of Instrumentation and Experimental Particle Physics, Lisbon, Portugal |

⁶

Karlsruhe Institute of Technology, Karlsruhe, Germany |

⁷

Instituto de Instrumentación para Imagen Molecular (I3M), CSIC, Universitat Politècnica de València, Valencia, Spain

Polytechnic University of Valencia

Instrumentation Institute For Molecular Image

⁸

Poznan Supercomputing and Networking Center, IBCh PAS, Poznan, Poland |

⁹

Slovak Academy of Sciences (IISAS), Institute of Informatics, Bratislava, Slovakia |

¹⁰

INFN CNAF, Bologna, Italy |

¹¹

CESNET, Prague, Czech Republic |

Publication type: Journal Article

Publication date: 2020-01-06

Institute of Electrical and Electronics Engineers (IEEE)

IEEE Access

scimago Q1

wos Q2

SJR: 0.849

CiteScore: 9.0

Impact factor: 3.6

ISSN: 21693536

DOI: 10.1109/access.2020.2964386

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General Materials Science

General Engineering

General Computer Science

Abstract

In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models.

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Bernalte-García Á. et al. A Cloud-Based Framework for Machine Learning Workloads and Applications // IEEE Access. 2020. Vol. 8. pp. 18681-18692.

GOST all authors (up to 50) Copy

Bernalte-García Á. et al. A Cloud-Based Framework for Machine Learning Workloads and Applications // IEEE Access. 2020. Vol. 8. pp. 18681-18692.

RIS |

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RIS Copy

TY - JOUR

DO - 10.1109/access.2020.2964386

UR - https://doi.org/10.1109/access.2020.2964386

TI - A Cloud-Based Framework for Machine Learning Workloads and Applications

T2 - IEEE Access

AU - Bernalte-García, Álvaro

AU - Marco de Lucas, Jesús

AU - Antonacci, M. Darryl

AU - zu Castell, Wolfgang

AU - David, Mario Alejandro

AU - Hardt, Marcus

AU - Lloret, Lara

AU - Moltó, Germán

AU - Plociennik, Marcin

AU - Tran, Viet

AU - Alic, Andy S.

AU - Caballer, Miguel

AU - Campos, I.

AU - Costantini, Alessandro

AU - Dlugolinsky, Stefan

AU - Duma, Doina Cristina

AU - Donvito, Giacinto

AU - Gomes, Jorge

AU - Heredia, Ignacio

AU - Ito, Keiichi

AU - Kozlov, Valentin M.

AU - Nguyen, Giang Trong

AU - Orviz Fernández, Pablo

AU - Šustr, Zdeněk

AU - Wolniewicz, Pawel

PY - 2020

DA - 2020/01/06

PB - Institute of Electrical and Electronics Engineers (IEEE)

SP - 18681-18692

VL - 8

SN - 2169-3536

ER -

BibTex

Cite this

BibTex (up to 50 authors) Copy

@article{2020_Bernalte-García,

author = {Álvaro Bernalte-García and Jesús Marco de Lucas and M. Darryl Antonacci and Wolfgang zu Castell and Mario Alejandro David and Marcus Hardt and Lara Lloret and Germán Moltó and Marcin Plociennik and Viet Tran and Andy S. Alic and Miguel Caballer and I. Campos and Alessandro Costantini and Stefan Dlugolinsky and Doina Cristina Duma and Giacinto Donvito and Jorge Gomes and Ignacio Heredia and Keiichi Ito and Valentin M. Kozlov and Giang Trong Nguyen and Pablo Orviz Fernández and Zdeněk Šustr and Pawel Wolniewicz and others},

title = {A Cloud-Based Framework for Machine Learning Workloads and Applications},

journal = {IEEE Access},

year = {2020},

volume = {8},

publisher = {Institute of Electrical and Electronics Engineers (IEEE)},

month = {jan},

url = {https://doi.org/10.1109/access.2020.2964386},

pages = {18681--18692},

doi = {10.1109/access.2020.2964386}

}

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Journal

IEEE Access

scimago Q1

wos Q2

SJR

0.849

CiteScore

9.0

Impact factor

3.6

ISSN

21693536 (Print, Electronic)