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DEEP-HybridDataCloud SIGNED

Designing and Enabling E-infrastructures for intensive Processing in a Hybrid DataCloud

Total Cost €

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EC-Contrib. €

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Partnership

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Project "DEEP-HybridDataCloud" data sheet

The following table provides information about the project.

Coordinator
AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS 

Organization address
address: CALLE SERRANO 117
city: MADRID
postcode: 28006
website: http://www.csic.es

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Spain [ES]
 Project website https://deep-hybrid-datacloud.eu
 Total cost 2˙988˙750 €
 EC max contribution 2˙988˙750 € (100%)
 Programme 1. H2020-EU.1.4.1.3. (Development, deployment and operation of ICT-based e-infrastructures)
 Code Call H2020-EINFRA-2017
 Funding Scheme RIA
 Starting year 2017
 Duration (year-month-day) from 2017-11-01   to  2020-04-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS ES (MADRID) coordinator 531˙250.00
2    ISTITUTO NAZIONALE DI FISICA NUCLEARE IT (FRASCATI) participant 375˙000.00
3    LABORATORIO DE INSTRUMENTACAO E FISICA EXPERIMENTAL DE PARTICULAS LIP PT (COIMBRA) participant 362˙500.00
4    USTAV INFORMATIKY, SLOVENSKA AKADEMIA VIED SK (BRATISLAVA) participant 271˙250.00
5    HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUNGSZENTRUM FUER GESUNDHEIT UND UMWELT GMBH DE (NEUHERBERG) participant 260˙000.00
6    INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ AKADEMII NAUK PL (POZNAN) participant 242˙500.00
7    KARLSRUHER INSTITUT FUER TECHNOLOGIE DE (KARLSRUHE) participant 241˙250.00
8    UNIVERSITAT POLITECNICA DE VALENCIA ES (VALENCIA) participant 237˙500.00
9    ATOS SPAIN SA ES (MADRID) participant 233˙750.00
10    CESNET ZAJMOVE SDRUZENI PRAVNICKYCH OSOB CZ (PRAHA 6) participant 233˙750.00

Map

 Project objective

The key concept proposed in the DEEP Hybrid DataCloud project is the need to support intensive computing techniques that require specialized HPC hardware, like GPUs or low latency interconnects, to explore very large datasets. A Hybrid Cloud approach enables the access to such resources that are not easily reachable by the researchers at the scale needed in the current EU e-infrastructure.

We also propose to deploy under the common label of “DEEP as a Service” a set of building blocks that enable the easy development of applications requiring these techniques: deep learning using neural networks, parallel post-processing of very large data, and analysis of massive online data streams.

Three pilot applications exploiting very large datasets in Biology, Physics and Network Security are proposed, and further pilots for dissemination into other areas like Medicine, Earth Observation, Astrophysics, and Citizen Science will be supported in a testbed with significant HPC resources, including latest generation GPUs, to evaluate the performance and scalability of the solutions. A DevOps approach will be implemented to provide the chain to ensure the quality of the software and services released, that will also be offered to the developers of research applications.

The project will evolve to TRL8 existing services and technologies at TRL6, including relevant contributions to the EOSC by the INDIGO-DataCloud H2020 project, that the project will enrich with new functionalities already available as prototypes, notably the support for GPUs and low latency interconnects. These services will be deployed in the project testbed, offered to the research communities linked to the project through pilot applications, and integrated under the EOSC framework, where they can be further scaled up in the future.

 Deliverables

List of deliverables.
Design for the DEEP as a Service solution Documents, reports 2020-03-24 10:44:42
Initial plan for Use Cases Documents, reports 2020-03-24 10:44:42
Definition of the Architecture of the Hybrid Cloud Documents, reports 2020-03-24 10:44:42
Status of Software releases Documents, reports 2020-03-24 10:44:42
Data Management considerations and initial plan Open Research Data Pilot 2020-03-24 10:44:42
Communication Measures: Plan and Implementation Status. Documents, reports 2020-03-24 10:44:42
First Software Platform Documents, reports 2020-03-24 10:44:42
Updated reports from global DEEP meetings. Documents, reports 2020-03-24 10:44:42
Pilot testbed Documents, reports 2020-03-24 10:44:42
State-of-the-art on Machine Learning frameworks Documents, reports 2020-03-24 10:44:42
Available Technologies for accelerators and HPC Documents, reports 2020-03-24 10:44:42
First prototype of the DEEP as a Service Documents, reports 2020-03-24 10:44:42
Dissemination and Exploitation Plan. Documents, reports 2020-03-24 10:44:42
High Level Hybrid Cloud solutions prototype Documents, reports 2020-03-24 10:44:42

Take a look to the deliverables list in detail:  detailed list of DEEP-HybridDataCloud deliverables.

 Publications

year authors and title journal last update
List of publications.
2020 Alvaro Lopez Garcia, Viet Tran, Andy S. Alic, Miguel Caballer, Isabel Campos Plasencia, Alessandro Costantini, Stefan Dlugolinsky, Doina Cristina Duma, Giacinto Donvito, Jorge Gomes, Ignacio Heredia Cacha, Jesus Marco De Lucas, Keiichi Ito, Valentin Y. Kozlov, Giang Nguyen, Pablo Orviz Fernandez, Zdenek Sustr, Pawel Wolniewicz, Marica Antonacci, Wolfgang Zu Castell, Mario David, Marcus Hardt, Lara
A cloud-based framework for machine learning workloads and applications
published pages: 1-1, ISSN: 2169-3536, DOI: 10.1109/access.2020.2964386
IEEE Access 2020-03-24
2018 Giang Nguyen, Binh Minh Nguyen, Dang Tran, Ladislav Hluchy
A heuristics approach to mine behavioural data logs in mobile malware detection system
published pages: 129-151, ISSN: 0169-023X, DOI: 10.1016/j.datak.2018.03.002
Data & Knowledge Engineering 115 2020-03-24
2018 Binh Minh Nguyen, Huan Phan, Duong Quang Ha, Giang Nguyen
An Information-centric Approach for Slice Monitoring from Edge Devices to Clouds
published pages: 326-335, ISSN: 1877-0509, DOI: 10.1016/j.procs.2018.04.046
Procedia Computer Science 130 2020-03-24
2017 Matej Babič, Ladislav Hluchy, Peter Krammer, Branko Matovič, Ravi Kumar, Pavel Kovač
New Method for Constructing a Visibility Graph-Network in 3D Space and a New Hybrid System of Modeling
published pages: 1107-1126, ISSN: 1335-9150, DOI: 10.4149/cai_2017_5_1107
Computing and Informatics 36/5 2020-03-24
2018 Nhuan Tran, Thang Nguyen, Binh Minh Nguyen, Giang Nguyen
A Multivariate Fuzzy Time Series Resource Forecast Model for Clouds using LSTM and Data Correlation Analysis
published pages: 636-645, ISSN: 1877-0509, DOI: 10.1016/j.procs.2018.07.298
Procedia Computer Science 126 2020-03-24
2018 Francisco Pando, Ignacio Heredia, Carlos Aedo Pérez, Mauricio Velayos Rodríguez, Lara Lloret Iglesias, Joel Calvo
Deep learning for weed identification based on seed images
published pages: e25749, ISSN: 2535-0897, DOI: 10.3897/biss.2.25749
Biodiversity Information Science and Standards 2 2020-03-24
2018 Donvito, Giacinto; Gomes, Jorge; Ferrer, A. Juan; Kozlov, Valentin; López García, Álvaro; Matyska, Ludek; Meyer, Norbert; Moltó, Germán; Tran, Viet; Castell, Wolfgang zu
DEEP-HybridDataCloud
published pages: , ISSN: , DOI:
ISC High Performance Computing 2020-03-24
2019 Giang Nguyen, Stefan Dlugolinsky, Martin Bobák, Viet Tran, Álvaro López García, Ignacio Heredia, Peter Malík, Ladislav Hluchý
Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey
published pages: , ISSN: 0269-2821, DOI: 10.1007/s10462-018-09679-z
Artificial Intelligence Review 2020-03-24
2018 Lara Lloret, Ignacio Heredia, Fernando Aguilar, Elisabeth Debusschere, Klaas Deneudt, Francisco Hernandez
Convolutional Neural Networks for Phytoplankton identification and classification
published pages: e25762, ISSN: 2535-0897, DOI: 10.3897/biss.2.25762
Biodiversity Information Science and Standards 2 2020-03-24
2018 Orviz, Pablo; López García, Álvaro; Duma, Doina Cristina; Donvito, Giacinto; David, Mario; Gomes, Jorge
A set of common software quality assurance baseline criteria for research projects
published pages: , ISSN: , DOI:
1 2020-03-24

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