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5a. ESA & WP9 : scale up study

5a. ESA & WP9 : scale up study. Welcome and introduction (15m) WP9 – Earth Observation Applications (5 0 m) ESA and WP9: infrastr, appl, TB 0/1 (5 0 m) Demonstration of EO applications (45m) ESA and WP9 : scale up , effort, recovery plan (5 0 m) Side ESA actions related to GRID (30m)

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5a. ESA & WP9 : scale up study

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  1. 5a. ESA & WP9: scale up study • Welcome and introduction (15m) • WP9 – Earth Observation Applications (50m) • ESA and WP9: infrastr, appl, TB 0/1 (50m) • Demonstration of EO applications (45m) • ESA and WP9: scale up, effort, recovery plan (50m) • Side ESA actions related to GRID (30m) • Reviewer separate meeting (60m) • Conclusions (30m) ESA DataGrid Review Frascati, 10 June 2002

  2. Summary • ESA and WP9 part 2 (45m) • DataGrid EO Scaling up vision (MM, 10m) • DataGrid ESA initial committed effort, present situation and revised plan, reporting and cost statement issues (LF, 20m)

  3. Task 9.5 Scaling Study • Outcome is the D9.6 Deliverable (Report) • Due by end of June • Main Topics: • Assesment of Testbed1 • Assessment of EO Requirements – Integration of SpaceGrid results • Recommendations for Scaling • General recommendations • Specific EO recommendations • EO Applications and Technology Survey • Application frameworks for GRID • Study on a EO Repository and Workflow Framework

  4. DataGrid EO Scaling up vision Marcello Mariucci University of Stuttgart Institute of Parallel and Distributed High-Performance Systems Applications of Parallel and Distributed Systems Department mariucci@informatik.uni-stuttgart.de

  5. Introduction • Earth Observation (EO) Services typically represent a sequence of EO Application Functions • Expl: • EO Application Functions are geographically distributed, and exchange large EO data products • Functions are part of large-scale, logically self-contained EO Application Systems, which are independently maintained by EO actors  B2B EAI Framework

  6. ARSENAL Project • Study on a large-scale B2B EAI • EO Application Framework • based on • Workflow(control flow) • Repository(meta data management) • Web Service and (unified access) • Grid technology (high-performance processing) For Details see: [M.Mariucci, B.Mitschang: On Making RAMSES and EO Application Framework. To be published in: The 2002 International Symposium on Information Systems and Engineering, ISE2002, July 2002, San Diego]

  7. Functional composition: Gathering EO Application Functionsto EO Application Components • Service assembly: Specification of EO application function relationships and workflows • Automation and consistent processing: Execution of EO application services bymeans of a workflow management system • Information Base: Constitution of a domain-specific, distri-buted database of the EO application’s structural and semantic information ARSENAL Concepts

  8. ARSENAL Layers

  9. ARSENAL Architecture

  10. GRID MOF UML E-Commerce Manufacturing Model Driven Architecture XML CORBA System Information Model Space Telecom Finance Web-Services HealthCare OMG Model Driven Architecture • ARSENAL is based on a Model Driven Architecture(OMG Standard, 2001)

  11. ARSENAL Development Process

  12. Users Groups Components DataStructures Structures Authorisations Services Functions Activities DataConnectors ControlConnectors ARSENAL Information Model (simplified)

  13. ARSENAL Repository ARSENAL Prototype ArsenalDesigner Grid, RMI, etc. Development Time EO ApplicationComponent MQSeries ODBC/SQL ModellingTool FDL WorkflowEngine Design Time JDBC/SQL Grid, CORBA, etc. EO ApplicationComponent Web Server MQSeries Run Time HTTP/SOAP Web Client

  14. Customer Application Application developer Customer Customer Portal Service Development GUI User User - - specific Application Environment Services Application Repository Execution Execution Information Service Manager Manager Development Development Collective Collective Control Flow Manager Data Flow Manager Data Flow Manager Resource Brokering Replication Manager Replication Manager Fabric Resources Fabric Resources Components Resource Manager Resource Manager Resource Manager Resource Manager Resource Manager Resource Resource Image Processor Image Processor CPU CPU … … Meteo Model Meteo Model On On - - Line Storage Line Storage Connectivity Fabric Fabric Value Adding Value Adding System Operator System Operator Service Provider Service Provider Outlook

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