#	Pagina
attuale pagina	/open-h2020/projects/212946/index.html

Opendata, web and dolomites

DEEP-HybridDataCloud SIGNED

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

Total Cost €

EC-Contrib. €

Partnership

Views

Outcomes and
results

DEEP-HybridDataCloud project word cloud

Explore the words cloud of the DEEP-HybridDataCloud project. It provides you a very rough idea of what is the project "DEEP-HybridDataCloud" about.

parallel prototypes functionalities label blocks indigo earth intensive security deploy latest trl6 testbed physics observation hpc neural software framework performance medicine service infrastructure communities developers solutions techniques linked learning gpus easily massive building cloud interconnects enrich datasets pilots streams latency devops technologies explore quality pilot generation eosc post biology scalability chain evolve hardware trl8 released requiring science citizen scaled computing data online astrophysics services deep hybrid contributions datacloud networks network specialized notably reachable

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 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.	ES (MADRID)	coordinator	531˙250.00
2	ISTITUTO NAZIONALE DI FISICA NUCLEARE ISTITUTO NAZIONALE DI FISICA NUCLEARE Organization address address: Via Enrico Fermi 54 city: FRASCATI postcode: 44 website: www.infn.it contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	IT (FRASCATI)	participant	375˙000.00
3	LABORATORIO DE INSTRUMENTACAO E FISICA EXPERIMENTAL DE PARTICULAS LIP LABORATORIO DE INSTRUMENTACAO E FISICA EXPERIMENTAL DE PARTICULAS LIP Organization address address: RUA LARGA 4 UNIVERSIDADE DE COIMBRA city: COIMBRA postcode: 3004 516 website: n.a. contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	PT (COIMBRA)	participant	362˙500.00
4	USTAV INFORMATIKY, SLOVENSKA AKADEMIA VIED USTAV INFORMATIKY, SLOVENSKA AKADEMIA VIED Organization address address: DUBRAVSKA CESTA 9 city: BRATISLAVA postcode: 845 07 website: n.a. contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	SK (BRATISLAVA)	participant	271˙250.00
5	HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUNGSZENTRUM FUER GESUNDHEIT UND UMWELT GMBH HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUNGSZENTRUM FUER GESUNDHEIT UND UMWELT GMBH Organization address address: INGOLSTADTER LANDSTRASSE 1 city: NEUHERBERG postcode: 85764 website: www.helmholtz-muenchen.de contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	DE (NEUHERBERG)	participant	260˙000.00
6	INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ AKADEMII NAUK INSTYTUT CHEMII BIOORGANICZNEJ POLSKIEJ AKADEMII NAUK Organization address address: NOSKOWSKIEGO 12-14 city: POZNAN postcode: 61 704 website: www.man.poznan.pl contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	PL (POZNAN)	participant	242˙500.00
7	KARLSRUHER INSTITUT FUER TECHNOLOGIE KARLSRUHER INSTITUT FUER TECHNOLOGIE Organization address address: KAISERSTRASSE 12 city: KARLSRUHE postcode: 76131 website: www.kit.edu contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	DE (KARLSRUHE)	participant	241˙250.00
8	UNIVERSITAT POLITECNICA DE VALENCIA UNIVERSITAT POLITECNICA DE VALENCIA Organization address address: CAMINO DE VERA SN EDIFICIO 3A city: VALENCIA postcode: 46022 website: www.upv.es contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	ES (VALENCIA)	participant	237˙500.00
9	ATOS SPAIN SA ATOS SPAIN SA Organization address address: CALLE DE ALBARRACIN 25 city: MADRID postcode: 28037 website: www.atos.net contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	ES (MADRID)	participant	233˙750.00
10	CESNET ZAJMOVE SDRUZENI PRAVNICKYCH OSOB CESNET ZAJMOVE SDRUZENI PRAVNICKYCH OSOB Organization address address: ZIKOVA 4 city: PRAHA 6 postcode: 160 00 website: n.a. contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	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

List of publications.
year	authors and title	journal	last update
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 BabiÄ, Ladislav Hluchy, Peter Krammer, Branko MatoviÄ, Ravi Kumar, Pavel KovaÄ 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 PÃ©rez, Mauricio Velayos RodrÃguez, 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; LÃ³pez GarcÃa, Ãlvaro; Matyska, Ludek; Meyer, Norbert; MoltÃ³, GermÃ¡n; Tran, Viet; Castell, Wolfgang zu DEEP-HybridDataCloud published pages: , ISSN: , DOI:	ISC High Performance Computing	2020-03-24
2019	Giang Nguyen, Stefan Dlugolinsky, Martin BobÃ¡k, Viet Tran, Ãlvaro LÃ³pez GarcÃa, Ignacio Heredia, Peter MalÃk, Ladislav HluchÃ½ 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; LÃ³pez GarcÃa, Ãlvaro; 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

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "DEEP-HYBRIDDATACLOUD" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "DEEP-HYBRIDDATACLOUD" are provided by the European Opendata Portal: CORDIS opendata.