Next Article in Journal
Evaluation of the Use of Sub-Pixel Offset Tracking Techniques to Monitor Landslides in Densely Vegetated Steeply Sloped Areas
Next Article in Special Issue
Thermal Imagery-Derived Surface Inundation Modeling to Assess Flood Risk in a Flood-Pulsed Savannah Watershed in Botswana and Namibia
Previous Article in Journal
Estimating Understory Temperatures Using MODIS LST in Mixed Cordilleran Forests
Previous Article in Special Issue
Time Series MODIS and in Situ Data Analysis for Mongolia Drought
Article Menu

Export Article

Open AccessArticle
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]   |  

Abstract

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
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top