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Cloud-enabling a Collaborative Research Platform: The GABBs Story

Published:09 July 2017Publication History

ABSTRACT

Modern cyberinfrastructures typically involve tightly integrated compute, storage and web application resources. They also form the basis of science gateways, which add their own science-specific processing or visualization capabilities. While some science gateways are intended as the central resource provider for a certain scientific community, others provide generic capabilities that are intended for further customization at each installation site. However, replicating their setup is a non-trivial task often involving specific operating system, software package and configuration choices while also requiring allocation of the actual physical computing resources. Cloud computing provides an attractive alternative, simplifying resource provision and enabling reliable replication. We describe our ongoing efforts to cloud-enable a geospatial science gateway hosting general-purpose software building blocks termed GABBs, that provide geospatial data management, analysis, visualization and processing capabilities. We describe the various compute and storage resources and software underlying these building blocks and our automation of the deployment, software installation and configuration of this science gateway on the Amazon Web Services (AWS) cloud platform. Some of the challenges that were encountered and resolved during this cloud-enabling process are also described.

References

  1. 2013. GABBs: NSF DIBBs: Geospatial Data Analysis Building Blocks. (2013). http://www.nsf.gov/awardsearch/showAward?AWD_ID=1261727Google ScholarGoogle Scholar
  2. L. Biehl and D. Landgrebe. 2002. MultiSpec - A Tool for Multispectral-Hyperspectral Image Data Analysis. Computers and Geosciences 28, 10 (2002), 1153--159. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. L. Biehl, L. Zhao, C. X. Song, and C. G. Panza. 2017. Cyberinfrastructure for the Collaborative Development of U2U Decision Support Tools. Journal of Climate Risk Management 15 (2017), 90--108.Google ScholarGoogle ScholarCross RefCross Ref
  4. T. Hertel and N. B. Villoria. 2014. GEOSHARE: Geospatial Open Source Hosting of Agriculture, Resource and Environmental Data for Discovery and Decision Making. (2014). https://mygeohub.org/resources/723Google ScholarGoogle Scholar
  5. R. Kalyanam, R. A. Campbell, S. P. Wilson, P. Meunier, L. Zhao, B. A. Hillery, and C. Song. 2016. Integrating HUBzero and iRODS: Geospatial Data Management for Collaborative Scientific Research. In The 2016 iRODS User Group Meeting.Google ScholarGoogle Scholar
  6. R. Kalyanam, L. Zhao, C. X. Song, Y. L. Wong, J. Lee, and N. B. Villoria. 2013. iData: A Community Geospatial Data Sharing Environment to Support Data-driven Science. In Proceedings of the Conference on Extreme Science and Engineering Discovery Environment. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. G. Klimeck, M. McLennan, S.P. Brophy, G. B. Adams III, and M. S. Lundstrom. 2008. nanoHUB.org: Advancing Education and Research in Nanotechnology. Computing in Science and Engineering 10, 5 (2008), 17--23. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. M. McLennan and R. Kennell. 2010. HUBzero: A Platform for Dissemination and Collaboration in Computational Science and Engineering. Computing in Science and Engineering 12, 2 (2010), 48--52. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. V. Merwade, W. Feng, L. Zhao, and C. Song. 2012. WaterHUB - A Resource for Students and Educators for Learning Hydrology. In Proceedings of the XSEDE12 Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Arcot Rajasekar, Reagan Moore, Chien-yi Hou, Christopher A Lee, Richard Marciano, Antoine de Torcy, Michael Wan, Wayne Schroeder, Sheau-Yen Chen, Lucas Gilbert, et al. 2010. iRODS Primer: integrated rule-oriented data system. Synthesis Lectures on Information Concepts, Retrieval, and Services 2, 1 (2010), 1--143. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. L. Zhao, C. X. Song, and L. Biehl. 2016. MyGeoHub Science Gateway for Spatial Data and a Model for Sustainability. In Gateways 2016.Google ScholarGoogle Scholar

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              • Published in

                cover image ACM Other conferences
                PEARC '17: Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact
                July 2017
                451 pages
                ISBN:9781450352727
                DOI:10.1145/3093338
                • General Chair:
                • David Hart

                Copyright © 2017 ACM

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                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 9 July 2017

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                • research-article
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                • Refereed limited

                Acceptance Rates

                PEARC '17 Paper Acceptance Rate54of79submissions,68%Overall Acceptance Rate133of202submissions,66%

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