Optimizing grid computing configuration and scheduling for geospatial analysis: An example with interpolating DEM

Abstract

Many geographic analyses are very time-consuming and do not scale well when large datasets are involved. For example, the interpolation of DEMs (digital evaluation model) for large geographic areas could become a problem in practical application, especially for web applications such as terrain visualization, where a fast response is required and computational demands exceed the capacity of a traditional single processing unit conducting serial processing. Therefore, high performance and parallel computing approaches, such as grid computing, were investigated to speed up the geographic analysis algorithms, such as DEM interpolation. The key for grid computing is to configure an optimized grid computing platform for the geospatial analysis and optimally schedule the geospatial tasks within a grid platform. However, there is no research focused on this. Using DEM interoperation as an example, we report our systematic research on configuring and scheduling a high performance grid computing platform to improve the performance of geographic analyses through a systematic study on how the number of cores, processors, grid nodes, different network connections and concurrent request impact the speedup of geospatial analyses. Condor, a grid middleware, is used to schedule the DEM interpolation tasks for different grid configurations. A Kansas raster-based DEM is used for a case study and an inverse distance weighting (IDW) algorithm is used in interpolation experiments.