Building an Elastic Parallel OGC Web Processing Service on a Cloud-Based Cluster: A Case Study of Remote Sensing Data Processing Service
| Autor: | Xinyue Ye, Baoxuan Jin, Liping Di, Jing Fu, Ziheng Sun, Guiwei Shao, Xicheng Tan, Zongyao Sha, Meng Gao, Meixia Deng |
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| Rok vydání: | 2015 |
| Předmět: |
Service (systems architecture)
Geospatial analysis Computer science computer.internet_protocol Geography Planning and Development TJ807-830 Cloud computing Management Monitoring Policy and Law TD194-195 computer.software_genre Renewable energy sources GeneralLiterature_MISCELLANEOUS Geospatial PDF GE1-350 Web Coverage Service Remote sensing Environmental effects of industries and plants parallel computing Renewable Energy Sustainability and the Environment business.industry Open Geospatial Consortium (OGC) Web Feature Service geospatial service cloud computing Geoprocessing Service-oriented architecture computer.file_format Environmental sciences Scalability Web Processing Service Web service business computer |
| Zdroj: | Sustainability, Vol 7, Iss 10, Pp 14245-14258 (2015) Sustainability Volume 7 Issue 10 Pages 14245-14258 |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su71014245 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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