Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/9/4/382 |
| _version_ | 1818958139939094528 |
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| author | Xicheng Tan Song Guo Liping Di Meixia Deng Fang Huang Xinyue Ye Ziheng Sun Weishu Gong Zongyao Sha Shaoming Pan |
| author_facet | Xicheng Tan Song Guo Liping Di Meixia Deng Fang Huang Xinyue Ye Ziheng Sun Weishu Gong Zongyao Sha Shaoming Pan |
| author_sort | Xicheng Tan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T11:21:00Z |
| format | Article |
| id | doaj.art-562753a7bc654dbe8983b7854ba48ba7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T11:21:00Z |
| publishDate | 2017-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-562753a7bc654dbe8983b7854ba48ba72022-12-21T19:42:31ZengMDPI AGRemote Sensing2072-42922017-04-019438210.3390/rs9040382rs9040382Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the CloudXicheng Tan0Song Guo1Liping Di2Meixia Deng3Fang Huang4Xinyue Ye5Ziheng Sun6Weishu Gong7Zongyao Sha8Shaoming Pan9International School of Software, Wuhan University, 37 Luoyu Road, Wuhan 430079, ChinaShanghai Academy of Spaceflight Technology, Yuanjiang Road 3888, Shanghai 201109, ChinaCenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USASchool of Resources & Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Ave., Chengdu 611731, ChinaDepartment of Geography, Kent State University, Kent, OH 44242, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USACenter for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USAInternational School of Software, Wuhan University, 37 Luoyu Road, Wuhan 430079, ChinaState Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaTo 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.http://www.mdpi.com/2072-4292/9/4/382geospatial serviceOpen Geospatial Consortium (OGC)remote sensing data processingcloud computingagentparallel computing |
| spellingShingle | Xicheng Tan Song Guo Liping Di Meixia Deng Fang Huang Xinyue Ye Ziheng Sun Weishu Gong Zongyao Sha Shaoming Pan Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud Remote Sensing geospatial service Open Geospatial Consortium (OGC) remote sensing data processing cloud computing agent parallel computing |
| title | Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud |
| title_full | Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud |
| title_fullStr | Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud |
| title_full_unstemmed | Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud |
| title_short | Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud |
| title_sort | parallel agent as a service p aaas based geospatial service in the cloud |
| topic | geospatial service Open Geospatial Consortium (OGC) remote sensing data processing cloud computing agent parallel computing |
| url | http://www.mdpi.com/2072-4292/9/4/382 |
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