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.
Tan, X., Guo, S., Di, L., Deng, M., Huang, F., Ye, X., Sun, Z., Gong, W., Sha, Z. and Pan, S.. "Parallel Agent-as-a-Service (P-AaaS) Based Geospatial Service in the Cloud," Remote Sensing, v.9, 2017, p. 382. doi:10.3390/rs9040382This material is based upon work supported by the National Science Foundation under Grant No. 1440294. Opinions, findings, conclusions or recommendations expressed are those of the authors and do not reflect the views of the NSF.