MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Using clouds to address grid limitations
Proceedings of the 6th international workshop on Middleware for grid computing
Retrieving and Indexing Spatial Data in the Cloud Computing Environment
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
GIS in the cloud: implementing a web map service on Google App Engine
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
Cloud computing for geosciences: deployment of GEOSS clearinghouse on Amazon's EC2
Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems
Evaluation of MapReduce for Gridding LIDAR Data
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Voronoi-Based Geospatial Query Processing with MapReduce
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
I/O performance of virtualized cloud environments
Proceedings of the second international workshop on Data intensive computing in the clouds
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The quest for better computing infrastructure for geospatial applications has been a constant endeavor for geoscientists. With the recent proliferation of cloud offerings, a range of new opportunities have become available. The challenge, however, is to make the best use of cloud platforms. Two directions are particularly important for addressing this challenge: a) developing new design approaches that are suitable for geoscience applications destined for the clouds, and b) accurately assessing the level of performance that can be expected when a given application is hosted on a given cloud platform with a specific configuration. This would enable scientists to better choose cloud solutions. In this paper, we focus on the latter direction. We use a typical data- and compute-intensive geoscience application, namely spatial interpolation, as a case study to assess the benefits of cloud computing for geoscience applications. We study the performance of the application on several types of cloud instances and provide a cost/benefit analysis that gives useful insights to geospatial and Earth scientists when they consider cloud options.