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Remote Sens. 2016, 8(8), 662; doi:10.3390/rs8080662

Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment

Department of Information Systems Engineering, Hansung University, Seoul 02876, Korea
Author to whom correspondence should be addressed.
Academic Editors: Chung-Ru Ho, Guoqing Zhou and Prasad S. Thenkabail
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)
View Full-Text   |   Download PDF [4883 KB, uploaded 16 August 2016]   |  


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. View Full-Text
Keywords: auto-scaling; cloud computing; OpenStack; satellite image processing auto-scaling; cloud computing; OpenStack; satellite image processing

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Kang, S.; Lee, K. Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment. Remote Sens. 2016, 8, 662.

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