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

Sustainability

Xicheng Tan⁴  Liping Di⁵  Meixia Deng⁵  Jing Fu⁶  Guiwei Shao⁶  Meng Gao⁷  Ziheng Sun⁵  Xinyue Ye¹  Zongyao Sha⁷  Baoxuan Jin²  Marc A. Rosen³ 

[1] Department of Geography, Kent State University, Kent, OH 44242, USA; E-Mail:;Yunnan Provincial Geomatics Center, 404, Huanchengxi Road, Kunming 650034, China; E-Mail:;id="af1-sustainability-07-14245">1Spatial Information and Digital Technology Department, International School of Software, Wuhan University, 37, Luoyu Road, Wuhan 430079, Chi;Spatial Information and Digital Technology Department, International School of Software, Wuhan University, 37, Luoyu Road, Wuhan 430079, China;Center for Spatial Information and Science Systems, George Mason University, 4087 University Dr, Fairfax, VA 22030, USA; E-Mails:;China Electric Power Research Institution, 143, Luoyu Road, Wuhan 430079, China; E-Mails:;International School of Software, Wuhan University, 37, Luoyu Road, Wuhan 430079, China; E-Mails:

关键词: Open Geospatial Consortium (OGC);    geospatial service;    cloud computing;    parallel computing;   

DOI  :  10.3390/su71014245

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.