Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud
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2017
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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.
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| Reference Key |
tan2017remoteparallel
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| Authors | Xicheng Tan;Song Guo;Liping Di;Meixia Deng;Fang Huang;Xinyue Ye;Ziheng Sun;Weishu Gong;Zongyao Sha;Shaoming Pan;Tan, Xicheng;Guo, Song;Di, Liping;Deng, Meixia;Huang, Fang;Ye, Xinyue;Sun, Ziheng;Gong, Weishu;Sha, Zongyao;Pan, Shaoming; |
| Journal | remote sensing |
| Year | 2017 |
| DOI |
10.3390/rs9040382
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| URL | |
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