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Article

A Performance Evaluation of a Geo-Spatial Image Processing Service Based on Open Source PaaS Cloud Computing Using Cloud Foundry on OpenStack

Department of Electronics and Information Engineering, Hansung University, Seoul 02876, Korea
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Remote Sens. 2018, 10(8), 1274; https://doi.org/10.3390/rs10081274
Received: 13 June 2018 / Revised: 11 August 2018 / Accepted: 13 August 2018 / Published: 13 August 2018
Recently, web application services based on cloud computing technologies are being offered. In the web-based application field of geo-spatial data management or processing, data processing services are produced or operated using various information communication technologies. Platform-as-a-Service (PaaS) is a type of cloud computing service model that provides a platform that allows service providers to implement, execute, and manage applications without the complexity of establishing and maintaining the lower-level infrastructure components, typically related to application development and launching. There are advantages, in terms of cost-effectiveness and service development expansion, of applying non-proprietary PaaS cloud computing. Nevertheless, there have not been many studies on the use of PaaS technologies to build geo-spatial application services. This study was based on open source PaaS technologies used in a geo-spatial image processing service, and it aimed to evaluate the performance of that service in relation to the Web Processing Service (WPS) 2.0 specification, based on the Open Geospatial Consortium (OGC) after a test application deployment using the configured service supported by a cloud environment. Using these components, the performance of an edge extraction algorithm on the test system in three cases, of 300, 500, and 700 threads, was assessed through a comparison test with another test system, in the same three cases, using Infrastructure-as-a-Service (IaaS) without Load Balancer-as-a-Service (LBaaS). According to the experiment results, in all the test cases of WPS execution considered in this study, the PaaS-based geo-spatial service had a greater performance and lower error rates than the IaaS-based cloud without LBaaS. View Full-Text
Keywords: cloud computing; Cloud Foundry; data processing; OGC WPS; OpenStack; optical remote sensing; performance test cloud computing; Cloud Foundry; data processing; OGC WPS; OpenStack; optical remote sensing; performance test
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MDPI and ACS Style

Lee, K.; Kim, K. A Performance Evaluation of a Geo-Spatial Image Processing Service Based on Open Source PaaS Cloud Computing Using Cloud Foundry on OpenStack. Remote Sens. 2018, 10, 1274. https://doi.org/10.3390/rs10081274

AMA Style

Lee K, Kim K. A Performance Evaluation of a Geo-Spatial Image Processing Service Based on Open Source PaaS Cloud Computing Using Cloud Foundry on OpenStack. Remote Sensing. 2018; 10(8):1274. https://doi.org/10.3390/rs10081274

Chicago/Turabian Style

Lee, Kiwon, and Kwangseob Kim. 2018. "A Performance Evaluation of a Geo-Spatial Image Processing Service Based on Open Source PaaS Cloud Computing Using Cloud Foundry on OpenStack" Remote Sensing 10, no. 8: 1274. https://doi.org/10.3390/rs10081274

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