Accelerating Science with Cloud Technologies in the ABoVE Science Cloud
Abstract
The Arctic-Boreal Vulnerability Experiment is a field campaign sponsored and initiated by NASA's Terrestrial Ecology Program. Research for ABoVE is leading to a greater understanding of ecosystem vulnerability and resilience to environmental change through integrating field-based studies, modeling, and data from airborne and satellite remote sensing. There are over 85 projects and more than 600 team members from multiple countries and institutions coming together to work on this common goal. In an effort to accelerate the pace of new Arctic science for researchers participating in the field campaign, the NASA Center for Climate Simulation has partnered with the NASA Carbon Cycle and Ecosystems Office to create a high performance science cloud. The ABoVE Science Cloud (ASC) combines high performance computing with emerging technologies and data management with tools for analyzing and processing geographic information to create an environment specifically designed for large-scale modeling, analysis of remote sensing data, and copious disk storage for "big data" with integrated data management. Through this architecture, the ASC provides an agile environment that contributes to data integration, geospatial product generation, modeling, data stewardship and long-term data preservation by aiding researchers through the entire process of the data lifecycle. The ASC can provide one scientist with hundreds of virtual machines custom-configured to accelerate computation and visualization. It also serves as a Geographical Information System proximate to petascale high-resolution imagery. Furthermore, by using the ABoVE Science Cloud as a shared and centralized resource, researchers reduce costs for their proposed work, making proposed research more competitive. Here we discuss the ABoVE Science Cloud, present the scientific requirements driving its development, and show examples of how the ASC is being used to meet the needs of the ABoVE campaign.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMIN34B..03H
- Keywords:
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- 1912 Data management;
- preservation;
- rescue;
- INFORMATICSDE: 1916 Data and information discovery;
- INFORMATICSDE: 1930 Data and information governance;
- INFORMATICSDE: 1942 Machine learning;
- INFORMATICS