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Remote Sens. 2017, 9(4), 382; doi:10.3390/rs9040382

Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud

1
International School of Software, Wuhan University, 37 Luoyu Road, Wuhan 430079, China
2
Engineering Research Center for Geo-Informatics and Digital Technology Authorized by National Administration of Surveying, Mapping and Geoinformation, Wuhan University, Wuhan 430079, China
3
Shanghai Academy of Spaceflight Technology, Yuanjiang Road 3888, Shanghai 201109, China
4
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
5
School of Resources & Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Ave., Chengdu 611731, China
6
Institute of Remote Sensing Big Data, Big Data Research Center, University of Electronic Science and Technology of China, 2006 Xiyuan Ave., Chengdu 611731, China
7
Department of Geography, Kent State University, Kent, OH 44242, USA
8
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Sangram Ganguly and Prasad S. Thenkabail
Received: 30 January 2017 / Revised: 7 April 2017 / Accepted: 13 April 2017 / Published: 19 April 2017
(This article belongs to the Special Issue Remote Sensing Big Data: Theory, Methods and Applications)
View Full-Text   |   Download PDF [5867 KB, uploaded 20 April 2017]   |  

Abstract

To optimize the efficiency of the geospatial service in the flood response decision making system, a Parallel Agent-as-a-Service (P-AaaS) method is proposed and implemented in the cloud. The prototype system and comparisons demonstrate the advantages of our approach over existing methods. The P-AaaS method includes both parallel architecture and a mechanism for adjusting the computational resources—the parallel geocomputing mechanism of the P-AaaS method used to execute a geospatial service and the execution algorithm of the P-AaaS based geospatial service chain, respectively. The P-AaaS based method has the following merits: (1) it inherits the advantages of the AaaS-based method (i.e., avoiding transfer of large volumes of remote sensing data or raster terrain data, agent migration, and intelligent conversion into services to improve domain expert collaboration); (2) it optimizes the low performance and the concurrent geoprocessing capability of the AaaS-based method, which is critical for special applications (e.g., highly concurrent applications and emergency response applications); and (3) it adjusts the computing resources dynamically according to the number and the performance requirements of concurrent requests, which allows the geospatial service chain to support a large number of concurrent requests by scaling up the cloud-based clusters in use and optimizes computing resources and costs by reducing the number of virtual machines (VMs) when the number of requests decreases. View Full-Text
Keywords: geospatial service; Open Geospatial Consortium (OGC); remote sensing data processing; cloud computing; agent; parallel computing geospatial service; Open Geospatial Consortium (OGC); remote sensing data processing; cloud computing; agent; parallel computing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Tan, X.; Guo, S.; Di, L.; Deng, M.; Huang, F.; Ye, X.; Sun, Z.; Gong, W.; Sha, Z.; Pan, S. Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud. Remote Sens. 2017, 9, 382.

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