A geospatial hybrid cloud platform based on multi-sourced computing and model resources for geosciences |
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| Authors: | Qunying Huang Jing Li Zhenlong Li |
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| Institution: | 1. Department of Geography, University of Wisconsin–Madison, Madison, WI, USA;2. Department of Geography and the Environment, University of Denver, Denver, CO, USA;3. Department of Geography, University of South Carolina, Columbia, SC, USA |
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| Abstract: | Cloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences. However, only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers: 1) selecting an appropriate cloud platform for a specific application could be challenging, as various cloud services are available and 2) existing general cloud platforms are not designed to support geoscience applications, algorithms and models. To tackle such barriers, this research aims to design a hybrid cloud computing (HCC) platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform. This platform can manage different types of underlying cloud infrastructure (e.g., private or public clouds), and enables geoscientists to test and leverage the cloud capabilities through a web interface. Additionally, the platform also provides different geospatial cloud services, such as workflow as a service, on the top of common cloud services (e.g., infrastructure as a service) provided by general cloud platforms. Therefore, geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly. A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources. |
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| Keywords: | Cloud computing Big Data geospatial cloud services workflow as a service (WaaS) geoprocessing as a service (GaaS) model as a service (MaaS) high-performance computing parallel computing |
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