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.
Highlights
Introduction and BackgroundWith the development of Web service technology [1], Web service based applications were introduced in the field of geographic information
We proposed a performance-adjustable Parallel Agent-as-a-Service (P-AaaS) based geospatial service approach that can address the performance problems of the sequential AaaS-based method
The performance of the AaaS-based method is much better than the traditional SOA-based methods, we still found that the sequential AaaS-based method experiences performance challenges when handling many concurrent requests
Summary
With the development of Web service technology [1], Web service based applications were introduced in the field of geographic information. The static, traditional SOA based geospatial service architecture causes the following serious problems in practical applications: 2. The fixed location of traditional SOA services reduces the efficiency of distributed geoprocessing services because they perform distributed geospatial data transfer inefficiently: the standard. SOA-based geospatial data transfer services such as WCS and WFS are extremely inefficient when transferring large spatial datasets. This low distributed data transfer efficiency can cause the geoprocessing tasks to fail. In situations where many agents are trying to migrate to and execute on the same host node, the AaaS-based method may be unable to handle the load Both the scalability and performance of the initial AaaS method require further improvements. Geocomputing Resource Adjustment Mechanism for P-AaaS Based Geospatial Services
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
