The emergence of Cloud Computing technologies brings a new information infrastructure to users.Providing geoprocessing functions in Cloud Computing platforms can bring scalable,on-demand,and costeffective geoprocessi...The emergence of Cloud Computing technologies brings a new information infrastructure to users.Providing geoprocessing functions in Cloud Computing platforms can bring scalable,on-demand,and costeffective geoprocessing services to geospatial users.This paper provides a comparative analysis of geoprocessing in Cloud Computing platformsMicrosoft Windows Azure and Google App Engine.The analysis compares differences in the data storage,architecture model,and development environment based on the experience to develop geoprocessing services in the two Cloud Computing platforms;emphasizes the importance of virtualization;recommends applications of hybrid geoprocessing Clouds,and suggests an interoperable solution on geoprocessing Cloud services.The comparison allows one to selectively utilize Cloud Computing platforms or hybrid Cloud pattern,once it is understood that the current development of geoprocessing Cloud services is restricted to specific Cloud Computing platforms with certain kinds of technologies.The performance evaluation is also performed over geoprocessing services deployed in public Cloud platforms.The tested services are developed using geoprocessing algorithms from different vendors,GeoSurf and Java Topology Suite.The evaluation results provide a valuable reference on providing elastic and cost-effective geoprocessing Cloud services.展开更多
基金This work was funded jointly by National Basic Research Program of China(2011CB707105)Project 41271397 and 41023001 supported by NSFCLIESMARS(Wuhan University)Special Research Funding.
文摘The emergence of Cloud Computing technologies brings a new information infrastructure to users.Providing geoprocessing functions in Cloud Computing platforms can bring scalable,on-demand,and costeffective geoprocessing services to geospatial users.This paper provides a comparative analysis of geoprocessing in Cloud Computing platformsMicrosoft Windows Azure and Google App Engine.The analysis compares differences in the data storage,architecture model,and development environment based on the experience to develop geoprocessing services in the two Cloud Computing platforms;emphasizes the importance of virtualization;recommends applications of hybrid geoprocessing Clouds,and suggests an interoperable solution on geoprocessing Cloud services.The comparison allows one to selectively utilize Cloud Computing platforms or hybrid Cloud pattern,once it is understood that the current development of geoprocessing Cloud services is restricted to specific Cloud Computing platforms with certain kinds of technologies.The performance evaluation is also performed over geoprocessing services deployed in public Cloud platforms.The tested services are developed using geoprocessing algorithms from different vendors,GeoSurf and Java Topology Suite.The evaluation results provide a valuable reference on providing elastic and cost-effective geoprocessing Cloud services.
