Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment
Department of Information Systems Engineering, Hansung University, Seoul 02876, Korea
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Academic Editors: Chung-Ru Ho, Guoqing Zhou and Prasad S. Thenkabail
Remote Sens. 2016, 8(8), 662; https://doi.org/10.3390/rs8080662
Received: 1 June 2016 / Revised: 1 August 2016 / Accepted: 15 August 2016 / Published: 16 August 2016
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
Cloud computing is a base platform for the distribution of large volumes of data and high-performance image processing on the Web. Despite wide applications in Web-based services and their many benefits, geo-spatial applications based on cloud computing technology are still developing. Auto-scaling realizes automatic scalability, i.e., the scale-out and scale-in processing of virtual servers in a cloud computing environment. This study investigates the applicability of auto-scaling to geo-based image processing algorithms by comparing the performance of a single virtual server and multiple auto-scaled virtual servers under identical experimental conditions. In this study, the cloud computing environment is built with OpenStack, and four algorithms from the Orfeo toolbox are used for practical geo-based image processing experiments. The auto-scaling results from all experimental performance tests demonstrate applicable significance with respect to cloud utilization concerning response time. Auto-scaling contributes to the development of web-based satellite image application services using cloud-based technologies.
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Keywords:
auto-scaling; cloud computing; OpenStack; satellite image processing
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MDPI and ACS Style
Kang, S.; Lee, K. Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment. Remote Sens. 2016, 8, 662. https://doi.org/10.3390/rs8080662
AMA Style
Kang S, Lee K. Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment. Remote Sensing. 2016; 8(8):662. https://doi.org/10.3390/rs8080662
Chicago/Turabian StyleKang, Sanggoo; Lee, Kiwon. 2016. "Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment" Remote Sens. 8, no. 8: 662. https://doi.org/10.3390/rs8080662
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