DOI QR코드

DOI QR Code

Performance Testing of Satellite Image Processing based on OGC WPS 2.0 in the OpenStack Cloud Environment

오픈스택 클라우드 환경 OGC WPS 2.0 기반 위성영상처리 성능측정 시험

  • Yoon, Gooseon (Department of Information Systems Engineering, Hansung University) ;
  • Kim, Kwangseob (Department of Information and Computer Engineering, Hansung University) ;
  • Lee, Kiwon (Department of Electronics and Information Engineering, Hansung University)
  • 윤구선 (한성대학교 정보시스템공학과) ;
  • 김광섭 (한성대학교 정보컴퓨터공학과) ;
  • 이기원 (한성대학교 전자정보공학과)
  • Received : 2016.11.18
  • Accepted : 2016.12.24
  • Published : 2016.12.31

Abstract

Many kinds of OGC-based web standards have been utilized in the lots of geo-spatial application fields for sharing and interoperable processing of large volume of data sets containing satellite images. As well, the number of cloud-based application services by on-demand processing of virtual machines is increasing. However, remote sensing applications using these two huge trends are globally on the initial stage. This study presents a practical linkage case with both aspects of OGC-based standard and cloud computing. Performance test is performed with the implementation result for cloud detection processing. Test objects are WPS 2.0 and two types of geo-based service environment such as web server in a single core and multiple virtual servers implemented on OpenStack cloud computing environment. Performance test unit by JMeter is five requests of GetCapabilities, DescribeProcess, Execute, GetStatus, GetResult in WPS 2.0. As the results, the performance measurement time in a cloud-based environment is faster than that of single server. It is expected that expansion of processing algorithms by WPS 2.0 and virtual processing is possible to target-oriented applications in the practical level.

웹 환경에서 위성영상정보를 포함하는 대용량 공간정보를 공유하거나, 상호호환성에 따라 처리하기 위한 공간정보 웹 표준들이 OGC에서 개발되어 다양한 분야에서 활용되고 있다. 한편 물리적인 컴퓨팅 자원을 직접 구매하지 않고도 웹을 통해 할당받아 필요할 때에만 접속하여 사용할 수 있는 클라우드 컴퓨팅 환경을 기반으로 하는 응용 서비스가 계속 증가하고 있다. 그러나 원격탐사 분야에서 이와 같은 두 가지의 중요한 기술 동향을 반영하는 연구 개발은 국제적으로도 초기 단계이다. 이번 연구에서는 실제로 이 두 가지 컴퓨팅 기술을 연계하는 사례를 제시하고 구름추출 서비스 구현 결과를 기반으로 하는 성능측정 시험결과를 제시하고자 한다. 실험에 적용한 대상 표준은 WPS 2.0 표준이며 성능측정 시험대상은 하나의 웹 서버 운영환경과 오픈소스 OpenStack 클라우드 컴퓨팅에 기반을 둔 가상서버운영 환경이다. JMeter를 이용한 성능측정 시험은 WPS 2.0의 GetCapabilities, DescribeProcess, Execute, GetStatus, GetResult 응답시간에 대한 실험을 수행하였다. 실험 결과로 다중 원격 서버와 가상 서버를 지원하는 클라우드 환경에서 상호운영 공간정보 처리를 위한 WPS 2.0 표준 적용의 경우도 좋은 성능을 나타냄을 알 수 있고, 추후 서버와 처리 알고리즘 기능 확장을 통하여 실무 적용이 가능할 것으로 기대한다.

Keywords

References

  1. Champion, N., 2012. Automatic Cloud Detection from Multi-Temporal Satellite Images: Towards The Use of PLIADES Time Series, ISPRSInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1: pp. 559-564.
  2. Chou, D. C., 2015. Cloud Computing: A Value Creation Model, Computer Standards and Interfaces, 38: 72-77. https://doi.org/10.1016/j.csi.2014.10.001
  3. Giuliani, G., S. Nativi, A. Lehmann and N. Ray, 2012. WPS Mediation: An Approach To Process Geospatial Data on Different Computing Backends, Computers and Geosciences, 47: 20-33. https://doi.org/10.1016/j.cageo.2011.10.009
  4. Jedlovec, G., 2009. Automated Detection of Clouds in Satellite Imagery, http://weather.msfc.nasa.gov/sport/journal/pdfs/2009_GRS_Jedlovec.pdf (Accessed November 16, 2016).
  5. JMeter. 2016. Apache JMeter Overview, http://jmeter.apache.org (Accessed November 16, 2016).
  6. Kang, S. and K. Lee, 2016. Auto-Scaling of Geo-Based Image Processing in an OpenStack Cloud Computing Environment, Remote Sensing, 8(8): 662. https://doi.org/10.3390/rs8080662
  7. KARI, 2016. Utilization of Satellite Images, http://www.kari.re.kr/kor/sub03_05.do (Accessed November 16, 2016).
  8. Li, W., S. Wang and V. Bhatia, 2016. Polarhub: A Large-Scale Web Crawling Engine For OGC Service Discovery In Cyberinfrastructure, Computers, Environment and Urban Systems, 59: 195-207. https://doi.org/10.1016/j.compenvurbsys.2016.07.004
  9. Mell, P. and T. Grance, 2011. The NIST Definition of Cloud Computing, NIST Special Publication 800-145, 7pp.
  10. Mueller, M. and B. Pross, 2015. OGC WPS 2.0 Interface Standard, Open Geospatial Consortium Inc., 133pp.
  11. OGC, 2014. Testbed 10 Performance of OGC$^{(R)}$ Services in the Cloud: The WMS, WMTS, and WPS cases, OGC 14-028r1, 47pp.
  12. OpenStack, 2016. OpenStack Software, https://www.openstack.org/software/ (Accessed November 16, 2016).
  13. Rautenbach, V., S. Coetzee and A. Iwaniak, 2013. Orchestrating OGC Web Services To Produce Thematic Maps In A Spatial Information Infrastructure, Computers, Environment and Urban Systems, 37: 107-120. https://doi.org/10.1016/j.compenvurbsys.2012.08.001
  14. Tan, X., L. Di, M. Deng, J. Fu, G. Shao, M. Gao, Z. Sun, X. Ye, Z. Sha and B. Jin, 2015. Building an Elastic Parallel OGC Web Processing Service on a Cloud-Based Cluster: A Case Study of Remote Sensing Data Processing Service, Sustainability, 7(10): 14245-14258. https://doi.org/10.3390/su71014245
  15. Westerholt, R. and B. Resch, 2014. Asynchronous Geospatial Processing: An Event-Driven Push-Based Architecture for the OGC Web Processing Service, Transactions in GIS, 19(3): 455-479. https://doi.org/10.1111/tgis.12104
  16. Xavier, E. M. A., F. J. Ariza-Lopez, and M. A. Urena-Camara, 2015. Web Service For Positional Quality Assessment: The Wps Tier, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 1: 257-262.
  17. Yoon, G. and K. Lee, 2015a. Testing Application of Web Processing Service (WPS) Standard to Satellite Image Processing, Korean Journal of Remote Sensing, 31(3): 245-254. https://doi.org/10.7780/kjrs.2015.31.3.4
  18. Yoon, G. and K. Lee, 2015b. WPS-based Satellite Image Processing on Web Framework and Cloud Computing Environment, Korean Journal of Remote Sensing, 31(6): 561-570. https://doi.org/10.7780/kjrs.2015.31.6.6
  19. Yoon, G. and K. Lee, 2016. Application of OGC WPS 2.0 to Geo-Spatial Web Services, Journal of the Korean Association of Geographic Information Studies, 19(3): 16-28. https://doi.org/10.11108/kagis.2016.19.3.016
  20. ZOO-Project. 2016. ZOO Introduction, http://zooproject.org/docs/intro.html (Accessed November 16, 2016).

Cited by

  1. A Performance Evaluation of a Geo-Spatial Image Processing Service Based on Open Source PaaS Cloud Computing Using Cloud Foundry on OpenStack vol.10, pp.8, 2018, https://doi.org/10.3390/rs10081274
  2. Performance Test of Asynchronous Process of OGC WPS 2.0: A Case Study for Geo-based Image Processing vol.33, pp.4, 2016, https://doi.org/10.7780/kjrs.2017.33.4.5
  3. 오픈소스 PaaS 클라우드와 공간정보 처리서비스 연계 기초 vol.20, pp.4, 2016, https://doi.org/10.11108/kagis.2017.20.4.024
  4. PaaS 클라우드 컴퓨팅 환경에서 전자정부 표준프레임워크 성능평가: 공간영상 정보처리 사례 vol.21, pp.4, 2016, https://doi.org/10.11108/kagis.2018.21.4.001