Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

A Performance Test of Mobile Cloud Service for Bayesian Image Fusion

베이지안 영상융합을 적용한 모바일 클라우드 성능실험

  • Kang, Sanggoo (Dept. of Information Systems Engineering, Hansung University) ;
  • Lee, Kiwon (Dept. of Information Systems Engineering, Hansung University)
  • 강상구 (한성대학교 정보시스템공학과) ;
  • 이기원 (한성대학교 정보시스템공학과)
  • Received : 2014.03.25
  • Accepted : 2014.05.15
  • Published : 2014.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

In recent days, trend technologies for cloud, bigdata, or mobile, as the important marketable keywords or paradigm in Information Communication Technology (ICT), are widely used and interrelated each other in the various types of platforms and web-based services. Especially, the combination of cloud and mobile is recognized as one of a profitable business models, holding benefits of their own. Despite these challenging aspects, there are a few application cases of this model dealing with geo-based data sets or imageries. Among many considering points for geo-based cloud application on mobile, this study focused on a performance test of mobile cloud of Bayesian image fusion algorithm with satellite images. Two kinds of cloud platform of Amazon and OpenStack were built for performance test by CPU time stamp. In fact, the scheme for performance test of mobile cloud is not established yet, so experiment conditions applied in this study are to check time stamp. As the result, it is revealed that performance in two platforms is almost same level. It is implied that open source mobile cloud services based on OpenStack are enough to apply further applications dealing with geo-based data sets.

현재 정보통신기술 분야의 핵심 용어라고 할 수 있는 클라우드, 빅데이터, 모바일 등이 다양한 플랫폼 및 서비스에 따라 상호 연결되면서 활용되고 있다. 특히 모바일과 연계된 클라우드는 모바일의 장점과 클라우드 컴퓨팅 기술 적용에 따른 장점을 모두 유지하고 향상시킬 수 있다. 그러나 아직 다른 나라에서도 공간영상정보의 처리나 분석 등과 같은 모바일 공공 클라우드 서비스를 제공하는 사례는 거의 없으며 실무적인 적용을 위한 실험 연구가 필요한 상황이다. 이번 연구에서는 위성영상정보의 베이지안 영상융합 기법을 적용한 모바일 클라우드 서비스 성능 실험을 수행하였다. 두 가지 플랫폼을 대상으로 하였는바, Amazon 클라우드 서비스 환경과 오픈소스 기반의 클라우드 컴퓨팅 환경인 OpenStack을 기반으로 한 자체적인 클라우드 환경을 구축하였다. 모바일 클라우드 성능 비교에 대한 기준이 아직 설정되어 있지 않는 실정이므로 가능한 간단하고 유사한 실험 조건을 적용한 실험 결과로 두 가지 클라우드 환경에서 처리 결과가 큰 차이는 없는 것으로 나타났다. 이는 오픈소스 기반의 모바일 클라우드 환경을 공간정보 서비스 분야에서도 충분히 적용할 수 있음을 의미한다.

Keywords

References

  1. Barnatt, C., 2010. A Brief Guide to Cloud Computing. Constable & Robinson Ltd., p.289.
  2. Cetl, V., 2013. Web and Cloud computing based Geospatial Services and Applications, Proc. of Workshop on Geospatial Data Infrastructure for Economic Development.
  3. Cutsforth, N., 2012. Simplified Elevation Analysis in the Cloud: Digitalglobe's Advanced Elevation Series, Imaging Notes, Fall 2012: 36-38.
  4. Evangelidis, K., K. Ntouros, S. Makridis, and C. Papatheodorou, 2014. Geospatial services in the Cloud, Computers and Geosciences, 63: 116-122. https://doi.org/10.1016/j.cageo.2013.10.007
  5. Fasbender, D. and P. Bogaert, 2007. Bayesian data fusion for adaptable image pansharpening, IEEE Transactions on Geoscience and Remote Sensing, 46(6): 1847-1857. https://doi.org/10.1109/TGRS.2008.917131
  6. Fasbender, D., V. Obsomer, P. Bogaert, and P. Defourny, 2009. Sensor and Data Fusion, ITech, Vienna, Austria.
  7. Fernando, N., S. W. Loke, and W. Rahayu, 2013. Mobile cloud computing: A survey, Future Generation Computer Systems, 29: 84-106. https://doi.org/10.1016/j.future.2012.05.023
  8. Huang, Q., C. Yang, K. Benedict, S. Chen, A. Rezgui, and J. Xie, 2013. Utilize cloud computing to support dust storm forecasting, International Journal of Digital Earth, 6(4): 338-355. https://doi.org/10.1080/17538947.2012.749949
  9. Iosup, A., S. Ostermann, N. Yigitbasi, R. Prodan, T. Fahringer, and D. Epema, 2011. Performance analysis of cloud computing services for manytasks scientific computing, IEEE Transactions on Parallel and Distributed Systems, 22(6): 931-945. https://doi.org/10.1109/TPDS.2011.66
  10. Kang, S., K. Lee, and Y. Kim, 2012. Preliminary performance testing of geo-spatial image parallel processing in the mobile cloud computing service, Korean Journal of Remote Sensing, 28(4): 467-475. (in Korean with English abstract) https://doi.org/10.7780/kjrs.2012.28.4.10
  11. Kang, S. and K. Lee, 2013a. Mobile app approach by open source stack for satellite images utilization, Remote Sensing Letters, 4(4): 648-656. https://doi.org/10.1080/2150704X.2013.781286
  12. Kang, S. and K. Lee, 2013b. Testing Implementation of Remote Sensing Image Analysis Processing Service on OpenStack of Open Source Cloud Platform, Journal of the Korean Association of Geographic Information Studies, 16(4): 141-152. https://doi.org/10.11108/kagis.2013.16.4.141
  13. Kang, S., K. Kim, and K. Lee, 2013. Tablet Application for Satellite Image Processing on Cloud Computing Platform, Proc. of International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 21-26 July.
  14. Kim, I.-H. and M.-H. Tsou, 2013. Enabling Digital Earth simulation models using cloud computing or grid computing; two approaches supporting high-performance GIS simulation frameworks, International Journal of Digital Earth, 6(4): 383-403. https://doi.org/10.1080/17538947.2013.783125
  15. Kim, K., S. Kang, and K. Lee. 2013. Geo-based image blending in a mobile cloud environment, Remote Sensing Letters, 4(11): 1117-1126. https://doi.org/10.1080/2150704X.2013.845922
  16. Krebs, R., C. Momm, and S. Kounev, 2013. Metrics and techniques for quantifying performance isolation in cloud environments, Science of Computer Programming, http://dx.doi.org/10.1016/j.scico.2013.08.003
  17. Lance, A., 2012. Comparing open source private cloud (IaaS) platforms, Presentation Material, Available at: http://cdn.oreillystatic.com/en/assets/1/event/80/Comparing Open Source Private CloudPlatforms Presentation.pdf (Accessed: 5 March 2014).
  18. Lee, K. 2012. Open Source Cloud Computing: An Experience Case of Geo-based Image Handling in Amazon Web Services, Korean Journal of Remote Sensing, 28(3): 337-346. https://doi.org/10.7780/kjrs.2012.28.3.337
  19. Lee, K. and S. Kang, 2013. Mobile cloud service of geo-based image processing functions: a test iPad implementation, Remote Sensing Letters, 4(9): 910-919. https://doi.org/10.1080/2150704X.2013.810821
  20. Navulur, K., 2013. Demystifying Cloud Computing for Remote Sensing Applications, Earthwide Communication LLC, August 2013: 14-19.
  21. OpenStack, 2014. OpenStack Installation Guide for Ubuntu 12.04 (LTS) havana, Available at: //docs.openstack.org/havana/install-guide/install/apt/openstack-install-guide-apt-havana.pdf (Accessed: 13 March 2014).
  22. OTB Development Team, 2013. The ORFEO Tool Box Software Guide Updated for OTB-3.20, 768p.
  23. Yang, C and C. Xu, 2013. Geoscience Application Challenges to Computing Infrastructure, In Spatial Cloud Computing: A Practical Approach, C. Yang, Q. Huang, Z. Li, C. Xu, and K. Liu (ed.), CRC Press: 1-15.

Cited by

  1. WPS-based Satellite Image Processing onWeb Framework and Cloud Computing Environment vol.31, pp.6, 2015, https://doi.org/10.7780/kjrs.2015.31.6.6
  2. Cloud-based Satellite Image Processing Service by Open Source Stack: A KARI Case vol.33, pp.4, 2014, https://doi.org/10.7780/kjrs.2017.33.4.1