Building an Elastic Parallel OGC Web Processing Service on a Cloud-Based Cluster: A Case Study of Remote Sensing Data Processing Service

Xicheng Tan, Liping Di, Meixia Deng, Jing Fu, Guiwei Shao, Meng Gao, Ziheng Sun, Xinyue Ye, Zongyao Sha and Baoxuan Jin
Additional contact information
Xicheng Tan: Spatial Information and Digital Technology Department, International School of Software, Wuhan University, 37, Luoyu Road, Wuhan 430079, China
Liping Di: Center for Spatial Information and Science Systems, George Mason University, 4087 University Dr, Fairfax, VA 22030, USA
Meixia Deng: Center for Spatial Information and Science Systems, George Mason University, 4087 University Dr, Fairfax, VA 22030, USA
Jing Fu: China Electric Power Research Institution, 143, Luoyu Road, Wuhan 430079, China
Guiwei Shao: China Electric Power Research Institution, 143, Luoyu Road, Wuhan 430079, China
Meng Gao: International School of Software, Wuhan University, 37, Luoyu Road, Wuhan 430079, China
Ziheng Sun: Center for Spatial Information and Science Systems, George Mason University, 4087 University Dr, Fairfax, VA 22030, USA
Xinyue Ye: Department of Geography, Kent State University, Kent, OH 44242, USA
Zongyao Sha: International School of Software, Wuhan University, 37, Luoyu Road, Wuhan 430079, China
Baoxuan Jin: Yunnan Provincial Geomatics Center, 404, Huanchengxi Road, Kunming 650034, China

Sustainability, 2015, vol. 7, issue 10, 1-14

Abstract: Since the Open Geospatial Consortium (OGC) proposed the geospatial Web Processing Service (WPS), standard OGC Web Service (OWS)-based geospatial processing has become the major type of distributed geospatial application. However, improving the performance and sustainability of the distributed geospatial applications has become the dominant challenge for OWSs. This paper presents the construction of an elastic parallel OGC WPS service on a cloud-based cluster and the designs of a high-performance, cloud-based WPS service architecture, the scalability scheme of the cloud, and the algorithm of the elastic parallel geoprocessing. Experiments of the remote sensing data processing service demonstrate that our proposed method can provide a higher-performance WPS service that uses less computing resources. Our proposed method can also help institutions reduce hardware costs, raise the rate of hardware usage, and conserve energy, which is important in building green and sustainable geospatial services or applications.

Keywords: Open Geospatial Consortium (OGC); geospatial service; cloud computing; parallel computing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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