Spatial cloud computing: how can the geospatial sciences use and help shape cloud computing?

TLDR

Geospatial sciences confront data, computing, concurrent, and spatiotemporal intensity challenges that demand an elastic, on‑demand infrastructure, and cloud computing offers a platform to integrate observation, algorithms, simulations, visualization, and decision support. The study examines how cloud computing can meet these intensities and how spatiotemporal principles can be leveraged to maximize its benefits for geospatial science. Four research examples illustrate data search and access, infrastructure configuration for intensive simulations, large‑scale result dissemination, and the application of spatiotemporal principles to support intensive applications. The paper concludes by outlining opportunities and challenges for spatial cloud computing.

Abstract

Abstract The geospatial sciences face grand information technology (IT) challenges in the twenty-first century: data intensity, computing intensity, concurrent access intensity and spatiotemporal intensity. These challenges require the readiness of a computing infrastructure that can: (1) better support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries; (2) provide real-time IT resources to enable real-time applications, such as emergency response; (3) deal with access spikes; and (4) provide more reliable and scalable service for massive numbers of concurrent users to advance public knowledge. The emergence of cloud computing provides a potential solution with an elastic, on-demand computing platform to integrate – observation systems, parameter extracting algorithms, phenomena simulations, analytical visualization and decision support, and to provide social impact and user feedback – the essential elements of the geospatial sciences. We discuss the utilization of cloud computing to support the intensities of geospatial sciences by reporting from our investigations on how cloud computing could enable the geospatial sciences and how spatiotemporal principles, the kernel of the geospatial sciences, could be utilized to ensure the benefits of cloud computing. Four research examples are presented to analyze how to: (1) search, access and utilize geospatial data; (2) configure computing infrastructure to enable the computability of intensive simulation models; (3) disseminate and utilize research results for massive numbers of concurrent users; and (4) adopt spatiotemporal principles to support spatiotemporal intensive applications. The paper concludes with a discussion of opportunities and challenges for spatial cloud computing (SCC).