research-article

Big data analytics for climate change and biodiversity in the EUBrazilCC federated cloud infrastructure

Authors:

Donatello Elia,

Francisco Vilar Brasileiro,

Ignacio BlanquerAuthors Info & Claims

CF '15: Proceedings of the 12th ACM International Conference on Computing Frontiers

Article No.: 52, Pages 1 - 8

https://doi.org/10.1145/2742854.2747282

Published: 06 May 2015 Publication History

Abstract

The analysis of large volumes of data is key for knowledge discovery in several scientific domains such as climate, astrophysics, life sciences among others. It requires a large set of computational and storage resources, as well as flexible and efficient software solutions able to dynamically exploit the available infrastructure and address issues related to data volume, distribution, velocity and heterogeneity. This paper presents a data-driven and cloud-based use case implemented in the context of the EUBrazilCC project for the analysis of climate change and biodiversity data. The use case architecture and main components, as well as a Platform as a Service (PaaS) framework for big data analytics named PDAS, together with its elastic deployment in the EUBrazilCC federated cloud infrastructure are presented and discussed in detail.

References

[1]

S. Fiore and G. Aloisio, 2011. Special section: Data management for eScience. FGCS 27(3), 290--291.

Digital Library

[2]

EUBrazilCC http://eubrazilcloudconnect.eu

[3]

M. E. S. Munoz, R. Giovanni, M. F. Siqueira, T. Sutton, P. Brewer, R. S. Pereira, D. A. L. Canhos, & V. P. Canhos, (2009) openModeller: a generic approach to species potential distribution modelling. GeoInformatica.

Digital Library

[4]

L. Cinquini, D. Crichton, C. Mattmann, J. Harney, G. Shipman, F. Wang, R. Ananthakrishnan, N. Miller, S. Denvil, M. Morgan, Z. Pobre, G. M. Bell, C. Doutriaux, R. Drach, D. Williams, P. Kershaw, S. Pascoe, E. Gonzalez, S. Fiore, R. Schweitzer, The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data, FGCS, ISSN 0167-739X, http://dx.doi.org/10.1016/j.future.2013.07.002.

[5]

K. E. Taylor, R. J. Stouffer, and G. A. Meehl, 2012, An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society 93(4), 485--498, http://dx.doi.org/10.1175/BAMS-D-11-00094.1.

[6]

R. K. Rew and G. P. Davis, 1990, The Unidata NetCDF: Software for scientific data access. In 6th Int. Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, American Meteorology Society, 33--40 (February 1990).

[7]

EMBRAPA https://www.embrapa.br/

[8]

Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., et al. A remote sensing surface energy balance algorithm for land (SEBAL): 1. Formulation. Journal of Hydrology, v. 212--213, p. 198--212, 1998a.

[9]

Bastiaanssen, W. G. M., Pelgrum, H., Wang, J., et al. A remote sensing surface energy balance algorithm for land (SEBAL): 2. Validation. Journal of Hydrology, v. 212--213, p. 213--229, 1998b.

[10]

speciesLink. Centro de Referencia em Informacao Ambiental, Campinas. http://splink.cria.org.br/.

[11]

fogbow http://www.fogbowcloud.org/

[12]

OCCI http://occi-wg.org/

[13]

R. Alfieri, R. Cecchini, V. Ciaschini, L. dell'Agnello, A. Frohner, K. Lorentey, and F. Spataro, From gridmapflle to voms: managing authorization in a grid environment. FGCS, 21(4): 549--558, 2005.

Digital Library

[14]

EGI FedCloud http://www.egi.eu/infrastructure/cloud/

[15]

M. Caballer, I. Blanquer, G. Molto and C. de Alfonso, Dynamic management of virtual infrastructures. Journal of Grid Computing, 2014, ISSN 1570-7873, 10.1007/s10723-014-9296-5.

Digital Library

[16]

DMTF Open Virtualization Format Specification version 1.0.0, DSP0243, 22 February 2009.

[17]

Ansible http://www.ansible.com.

[18]

J. V. Carrion, G. Molto, C. De Alfonso, M. Caballer, V. Hernandez, A Generic Catalog and Repository Service for Virtual Machine Images, in: 2nd International ICST Conference on Cloud Computing (CloudComp 2010), (2010).

[19]

S. Fiore, A. D'Anca, C. Palazzo, I. Foster, D. Williams and G. Aloisio, 2013, Ophidia: Toward Big Data Analytics for eScience. ICCS 2013, 2376--2385.

[20]

S. Fiore, C. Palazzo, A. D'Anca, I. T. Foster, D. N. Williams, G. Aloisio: A big data analytics framework for scientific data management. IEEE BigData Conference 2013: 1--8

[21]

S. Fiore, A. D'Anca, D. Elia, C. Palazzo, I. Foster, D. Williams, G. Aloisio, Ophidia: A Full Software Stack for Scientific Data Analytics, proc. of the 2014 International Conference on High Performance Computing & Simulation (HPCS 2014), July 21--25, 2014, Bologna, Italy, pp. 343--350, ISBN: 978-1-4799-5311-0.

[22]

W. Gropp, E. Lusk, and A. Skjellum, Using MPI: Portable Parallel Programming with the Message-Passing Interface, MIT Press, 1999.

Digital Library

[23]

W. Gropp, E. Lusk, and R. Thakur, Using MPI-2: Advanced Features of the Message-Passing Interface, MIT Press, 1999.

Digital Library

[24]

The GNU Scientific Library (GSL) http://www.gnu.org/software/gsl/.

[25]

Satish Balay, Jed Brown, Kris Buschelman, William D. Gropp, et al., PETSc web page http://www.mcs.anl.gov/petsc, 2012.

[26]

SpeciesLink service http://tapir.cria.org.br/tapirlink/tapir.php/specieslink

[27]

Emission scenarios http://www.wmo.int/pages/themes/climate/emission_scenarios.php

Cited By

Jaya J(2021)Application of Deep Learning in Cloud SecurityDeep Learning Approaches to Cloud Security10.1002/9781119760542.ch12(189-205)Online publication date: 24-Dec-2021
https://doi.org/10.1002/9781119760542.ch12
Fiore SElia DBlanquer IBrasileiro FNuzzo ANassisi PRufino ISeijmonsbergen AAnders Nde O. Galvão Cde B.L. Cunha JCaballer MSousa-Baena MCanhos VAloisio G(2017)BioClimate: A Science Gateway for Climate Change and Biodiversity research in the EUBrazilCloudConnect projectFuture Generation Computer Systems10.1016/j.future.2017.11.034Online publication date: Dec-2017
https://doi.org/10.1016/j.future.2017.11.034
Vivas JBrasileiro FBarros AFarias da Silva GNóbrega Jr MNeto FBlanquer ITorres EAloisio GFiore SBadia RLezzi DGomes ACała JJulia de Lima MUrurahy C(2016)EUBrazilCC Federated CloudDeveloping Interoperable and Federated Cloud Architecture10.4018/978-1-5225-0153-4.ch008(220-251)Online publication date: 2016
https://doi.org/10.4018/978-1-5225-0153-4.ch008

Index Terms

Big data analytics for climate change and biodiversity in the EUBrazilCC federated cloud infrastructure

Recommendations

Challenges and Benefits of Deploying Big Data Analytics in the Cloud for Business Intelligence

Cloud computing and big data analytics are, without a doubt, two of the most important technologies to enter the mainstream IT industry in recent years. Surprisingly, the two technologies are coming together to deliver powerful results and benefits for ...
Big data analytics in Cloud computing: an overview
Abstract
Big Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Every day a huge amount of data is produced from different sources. This data is so big in size that traditional processing tools are unable ...
Responsible Big Data Analytics for E-Business Services
ICBDR '21: Proceedings of the 5th International Conference on Big Data Research

This paper examines responsible big data analytics for e-business services and looks at how to use responsible big data analytics to obtain responsible e-business services. It addresses why responsibility matters to big data analytics and e-business ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CF '15: Proceedings of the 12th ACM International Conference on Computing Frontiers

May 2015

413 pages

ISBN:9781450333580

DOI:10.1145/2742854

General Chairs:
Claudia Di Napoli
Istituto di Calcolo e Reti ad Alte Prestazioni, CNR, ITALY
,
Valentina Salapura
IBM T. J. Watson Research Center
,
Program Chairs:
Hubertus Franke
IBM T.J.Watson Research Center
,
Rui Hou
Institute for Computing Technology, Chinese Academy of Sciences, PRC

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMICRO: ACM Special Interest Group on Microarchitectural Research and Processing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 May 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

CF'15

Sponsor:

SIGMICRO

CF'15: Computing Frontiers Conference

May 18 - 21, 2015

Ischia, Italy

Acceptance Rates

CF '15 Paper Acceptance Rate 33 of 96 submissions, 34%;

Overall Acceptance Rate 273 of 785 submissions, 35%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
398
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 29 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Jaya J(2021)Application of Deep Learning in Cloud SecurityDeep Learning Approaches to Cloud Security10.1002/9781119760542.ch12(189-205)Online publication date: 24-Dec-2021
https://doi.org/10.1002/9781119760542.ch12
Fiore SElia DBlanquer IBrasileiro FNuzzo ANassisi PRufino ISeijmonsbergen AAnders Nde O. Galvão Cde B.L. Cunha JCaballer MSousa-Baena MCanhos VAloisio G(2017)BioClimate: A Science Gateway for Climate Change and Biodiversity research in the EUBrazilCloudConnect projectFuture Generation Computer Systems10.1016/j.future.2017.11.034Online publication date: Dec-2017
https://doi.org/10.1016/j.future.2017.11.034
Vivas JBrasileiro FBarros AFarias da Silva GNóbrega Jr MNeto FBlanquer ITorres EAloisio GFiore SBadia RLezzi DGomes ACała JJulia de Lima MUrurahy C(2016)EUBrazilCC Federated CloudDeveloping Interoperable and Federated Cloud Architecture10.4018/978-1-5225-0153-4.ch008(220-251)Online publication date: 2016
https://doi.org/10.4018/978-1-5225-0153-4.ch008

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents