{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:10:54Z","timestamp":1760213454166,"version":"build-2065373602"},"reference-count":20,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2016,8,16]],"date-time":"2016-08-16T00:00:00Z","timestamp":1471305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Land Space Information Research Program from the Ministry of Land, Infrastructure and Transport, Republic of Korea","award":["14NSIP-B080144-01"],"award-info":[{"award-number":["14NSIP-B080144-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs8080662","type":"journal-article","created":{"date-parts":[[2016,8,16]],"date-time":"2016-08-16T10:03:21Z","timestamp":1471341801000},"page":"662","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment"],"prefix":"10.3390","volume":"8","author":[{"given":"Sanggoo","family":"Kang","sequence":"first","affiliation":[{"name":"Department of Information Systems Engineering, Hansung University, Seoul 02876, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8586-4750","authenticated-orcid":false,"given":"Kiwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Information Systems Engineering, Hansung University, Seoul 02876, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,16]]},"reference":[{"key":"ref_1","unstructured":"Mell, P., and Grance, T. 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Available online: http:\/\/docs.openstack.org\/admin-guide-cloud\/telemetry-measurements.html."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/8\/662\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:28:32Z","timestamp":1760210912000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/8\/662"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8,16]]},"references-count":20,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2016,8]]}},"alternative-id":["rs8080662"],"URL":"https:\/\/doi.org\/10.3390\/rs8080662","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2016,8,16]]}}}