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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xicheng Tan & Liping Di & Meixia Deng & Jing Fu & Guiwei Shao & Meng Gao & Ziheng Sun & Xinyue Ye & Zongyao Sha & Baoxuan Jin, 2015. "Building an Elastic Parallel OGC Web Processing Service on a Cloud-Based Cluster: A Case Study of Remote Sensing Data Processing Service," Sustainability, MDPI, vol. 7(10), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:10:p:14245-14258:d:57506
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    References listed on IDEAS

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    1. Menno-Jan Kraak, 2004. "The role of the map in a Web-GIS environment," Journal of Geographical Systems, Springer, vol. 6(2), pages 83-93, June.
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    Cited by:

    1. Feng Zhang & Jingwei Zhou & Renyi Liu & Zhenhong Du & Xinyue Ye, 2016. "A New Design of High-Performance Large-Scale GIS Computing at a Finer Spatial Granularity: A Case Study of Spatial Join with Spark for Sustainability," Sustainability, MDPI, vol. 8(9), pages 1-19, September.
    2. Junhu Ruan & Felix T. S. Chan & Fangwei Zhu & Xuping Wang & Jing Yang, 2016. "A Visualization Review of Cloud Computing Algorithms in the Last Decade," Sustainability, MDPI, vol. 8(10), pages 1-16, October.

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