Abstract
Grid computing has widely evolved over the past years, and its capabilities have found their way even into business products and are no longer relegated to scientific applications. Today, grid computing technology is not restricted to a set of specific grid open source or industrial products, but rather it is comprised of a set of capabilities virtually within any kind of software to create shared and highly collaborative production environments. These environments are focused on computational (workload) capabilities and the integration of information (data) into those computational capabilities. An active grid computing application field is the fully virtualization of scientific instruments in order to increase their availability and decrease operational and maintaining costs. Computational and information grids allow to manage real-world objects in a service-oriented way using industrial world-spread standards.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ascione, I., Giunta, G., Montella, R., Mariani, P., & Riccio, A. (2006). A grid computing based virtual laboratory for environmental simulations. Proceedings of 12th International Euro-Par 2006, Dresden, Germany, August/September 2006. LNCS 4128, Springer 2006.
Doty, B. E., & Kinter, J. L., III (1995). Geophysical data analysis and visualization using GrADS. In E. P. Szuszczewicz & J. H. Bredekamp (Eds.), Visualization techniques in space and atmospheric sciences (pp. 209–219). Washington, DC: NASA.
Foster, I., Freeman, T., Keahey, K., Scheftner, D., Sotomayor, B., & Zhang, X. (May 2006). Virtual clusters for grid communities. Singapore: CCGRID.
Gallagher, J., Potter, N., & Sgouros, T. (2004). DAP data model specification DRAFT. Retrieved November 6, 2004, Rev., 1.68, from http://www.opendap.org.
Gallagher, J., Potter, N., West, P., Garcia, J., & Fox, P. (2006). OPeNDAP’s Server4: Building a high performance data server for the DAP using existing software. AGU Meeting in San Francisco, San Francisco, CA.
Giunta, G., Laccetti, G., & Montella, R. (2008). A grid-based service oriented environmental modeling laboratory for research and production applications. In R. Wyrzykowski, J. Dongarra, K. Karczewski, & J. Wasniewski (Eds.), Chapter of Parallel Processing and Applied Mathematics 2007 (pp. 951–960). Lecture Notes in Computer Science n. 4967. New York, NY: Springer.
Holland, L., Gotway, J. H., Brown, B., & Bullock, R. A. Toolkit for model evaluation (National Center for Atmospheric Research Boulder, CO 80307).
Llorente, I. M. (July 2008). Towards a new model for the infrastructure grid. Panel From Grids to Cloud Services in the International Advanced Research Workshop on High Performance Computing and Grids, Cetraro, Italy.
Llorente, I. M. (July 2008). Cloud computing for on-demand resource provisioning. International Advanced Research Workshop on High Performance Computing and Grids, Cetraro, Italy.
Montella, R. (May 2007). Development of a GT4-based resource broker service: An application to on-demand weather and marine forecasting (Vol. LNCS 4459 of LNCS). New York, NY: Springer.
Montella, R., Giunta, G., & Riccio, A. (June 2007). Using grid computing based components in on demand environmental data delivery. ACM Proceedings About Upgrade Content Network HPDC2008 Workshop. Monterey Bay, CA, USA.
Montella, R., Agrillo, G., & Di Lauro, R. (April 2008a). Abstract instrument framework: Java interface for instrument abstraction (DSA Tech. Rep.) Napoli.
Montella, R., Agrillo, G., Mastrangelo, D., & Menna, M. (2008b). A globus toolkit 4 based instrument service for environmental data acquisition and distribution. Proceedings of Upgrade Content Workshop HPDC2008, Boston, MA.
Ramakrishnan, L., Blanton, B., Lander, H., Luettich, R., Reed, D., & Thorpe, S. (2006). Real-time storm surge ensemble modeling in a grid environment.
Sotomayor, B., Keahey, K., & Foster, I. (June 2008). Combining batch execution and leasing using virtual machines. ACM/IEEE International Symposium on High Performance Distributed Computing 2008 (HPDC 2008), Boston, MA.
Warren, R. (2007). Development and illustrative outputs of the community integrated assessment system (cias), a multi-institutional modular integrated assessment approach for modelling climate change. Environmental Modelling and Software, 20, 1–19.
Wielgosz, J., & Doty, J. A. B. (2003). The grads-dods server: An open-source tool for distributed data access and analysis.
Wielgosz, J. (2004). Anagram: A modular java framework for high-performance scientific data servers.
Youseff, L., Wolski, R., Gorda, B., & Krintz, C. (December 2006). Paravirtualization for HPC systems XHPC. Workshop on XEN in High-Performance Cluster and Grid Computing.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Montella, R., Giunta, G., Laccetti, G. (2010). Multidimensional Environmental Data Resource Brokering on Computational Grids and Scientific Clouds. In: Furht, B., Escalante, A. (eds) Handbook of Cloud Computing. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6524-0_20
Download citation
DOI: https://doi.org/10.1007/978-1-4419-6524-0_20
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-6523-3
Online ISBN: 978-1-4419-6524-0
eBook Packages: Computer ScienceComputer Science (R0)