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Parallelization of web processing services on cloud computing: A case study of Geostatistical Methods
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Resumo(s)
In the last decade the publication of geographic information has increased in Internet,
especially with the emergence of new technologies to share information. This
information requires the use of technologies of geoprocessing online that use new
platforms such as Cloud Computing. This thesis work evaluates the parallelization of
geoprocesses on the Cloud platform Amazon Web Service (AWS), through OGC
Web Processing Services (WPS) using the 52North WPS framework. This evaluation
is performed using a new implementation of a Geostatistical library in Java with
parallelization capabilities. The geoprocessing is tested by incrementing the number
of micro instances on the Cloud through GridGain technology. The Geostatistical library obtains similar interpolated values compared with the
software ArcGIS. In the Inverse Distance Weight (IDW) and Radial Basis Functions
(RBF) methods were not found differences. In the Ordinary and Universal Kriging
methods differences have been found of 0.01% regarding the Root Mean Square
(RMS) error.The parallelization process demonstrates that the duration of the interpolation
decreases when the number of nodes increases. The duration behavior depends on the
size of input dataset and the number of pixels to be interpolated. The maximum
reduction in time was found with the largest configuration used in the research
(1.000.000 of pixels and a dataset of 10.000 points). The execution time decreased in
83% working with 10 nodes in the Ordinary Kriging and IDW methods. However,
the differences in duration working with 5 nodes and 10 nodes were not statistically
significant. The reductions with 5 nodes were 72% and 71% in the Ordinary Kriging
and IDW methods respectively. Finally, the experiments show that the geoprocessing on Cloud Computing is feasible
using the WPS interface. The performance of the geostatistical methods deployed
through the WPS services can improve by the parallelization technique. This thesis
proves that the parallelization on the Cloud is viable using a Grid configuration. The
evaluation also showed that parallelization of geoprocesses on the Cloud for
academic purposes is inexpensive using Amazon AWS platform.
Descrição
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
Palavras-chave
Web Processing Services Parallelization Algorithms Interpolation Geostatistics Cloud Computing