Geoprocessing in Cloud Computing platforms – a comparative analysis

Figures & data

Figure 1.  A general architecture for geoprocessing in Cloud platforms.

Table 1. Typical cases on geospatial Cloud Computing.

Geospatial Cloud	Geospatial application	Programming language	Service interface	Types of Cloud	Cloud platform
52 North geoprocessing Cloud (Baranski et al. 2009)	Buffer analysis by JTS	Java	OGC WPS	Public	Google App Engine
Spatial data Cloud (Wang et al. 2009)	Spatial data indexing and management	Java	OGC WKB/WKT	Public	Google App Engine
Payment model in geoprocessing Cloud (Baranski et al. 2010)	Pay-per-use revenue model for geoprocessing services	Java	OGC WPS/OASIS XACML	Public	Amazon EC2
Cloud-enabled geoprocessing (Gong et al. 2010)	Terrain slope computation	Java/C#	OGC WPS	Public	Microsoft Azure
FGDC GeoCloud (Huang et al. 2010)	GEOSS Clearinghouse	Java	OGC Catalogue Service for the Web (CSW)	Public	Amazon EC2
Cloud-enabled SDI (Schäffer et al. 2010)	Buffer and intersection analysis by JTS	Java	OGC WPS	Public	Amazon EC2
Cloud-enabled WMS (Blower 2010)	Web Map Service	Java	OGC WMS	Public	Google App Engine
QoS in geoprocessing Cloud (Baranski et al. 2011)	Coordinate transformation	Java	OGC WPS	Hybrid Cloud	Private Cloud/Amazon EC2
FGDC, US Federal Geographic Data Committee; GEOSS, Global Earth Observation System of Systems; JTS, Java Topology Suite; OASIS, Organization for the Advancement of Structured Information Standards; OGC, Open Geospatial Consortium; QoS, Quality of Service; SDI, Spatial Data Infrastructures; WKB, well-known binary; WKT, well-known text; WMS, Web Map Service; WPS, the Web Processing Service; XACML, eXtensible Access Control Markup Language.

Figure 2.  WPS Execution request using the HTTP GET method.

Figure 3.  Workflow on using storage services in Web and Worker roles.

Figure 4.  Workflow on using storage services in Google App Engine.

Figure 5.  User interface for geoprocessing Cloud services in Google.

Figure 6.  Performance tests of GeoSurf and Java Topology Suite services (20 executions).

Figure 7.  Costs of GeoSurf and Java Topology Suite services (20 executions).

Figure 8.  Performance tests of GeoSurf and Java Topology Suite services (different sizes).

Figure 9.  Costs of GeoSurf and Java Topology Suite services (different sizes).

Figure 10.  Performance tests of Java Topology Suite services in Google App Engine and Azure (20 executions).

Figure 11.  Performance tests of Java Topology Suite services in Google App Engine and Azure (different sizes).

Baranski , B. , B. Schaeffer , and R. Redweik . 2009 . “ Geoprocessing in the Clouds .” In GeoInformatics , E. van Rees , 12 , 386 – 395 . Emmeloord , , Netherlands : CMedia B.V .

 Google Scholar

Wang , Y. , S. Wang , and D. Zhou . 2009 . “ Retrieving and Indexing Spatial Data in the Cloud Computing Environment .” In Proceedings of the 1st International Conference on Cloud Computing (CloudCom 2009) , M. G. Jaatun , G. Zhao , and C. Rong , 5931 , 322 – 331 . Berlin : Springer Verlag, Lecture Notes in Computer Science (LNCS) .

 Google Scholar

Baranski , B. , T. Deelmann , and B. Schäffer . 2010 . “ Pay-Per-Use Revenue Models for Geoprocessing Services in the Cloud .” Proceedings of the First International Workshop on Pervasive Web Mapping, Geoprocessing and Services (WebMGS 2010) , Como , Italy , August 10–12 , 6

 Google Scholar

Gong , J. , P. Yue , and H. Zhou . 2010 . “ Geoprocessing in the Microsoft Cloud Computing Platform – Azure .” Proceedings of the joint symposium of ISPRS Technical Commission IV & AutoCarto 2010 , Orlando , FL , November 15–19 , 2010 , 6

 Google Scholar

Huang , Q. , C. Yang , D. Nebert , K. Liu , and H. Wu . 2010 . “ Cloud Computing for Geosciences: Deployment of GEOSS Clearinghouse on Amazon's EC2 .” Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic Information Systems, HPDGIS'10 , San Jose , CA , November 3–5 , 2010 , 35 – 38 .

 Google Scholar

Schäffer , B. , Baranski , B. and Foerster , T. 2010 . “ Towards Spatial Data Infrastructures in the Clouds ” . In Geospatial Thinking: Proceedings of the Thirteenth AGILE International Conference on Geographic Information Science , Edited by: Painho , M. , Santos , M. and Pundt , H. 399 – 418 . Guimarães , Portugal, Berlin : Springer Verlag Lecture Notes in Geoinformation and Cartography .

 Google Scholar

Blower , J. D. 2010 . “ GIS in the Cloud: Implementing a Web Map Service on Google App Engine .” Proceedings of the First International Conference on Computing for Geospatial Research and Application , Washington , DC , June 21–23 , 4

 Google Scholar

Baranski , B. , Foerster , T. , Schäffer , B. and Lange , K. 2011 . Matching INSPIRE Quality of Service Requirements with Hybrid Clouds . Transactions in GIS , 15 ( s1 ) : 125 – 142 . doi: 10.1111/j.1467-9671.2011.01265.x

 Web of Science ®Google Scholar