1. Workflow in Grid Systems Workshop Dave Berry, Research Manager UK National e-Science Centre GGF10, Mar 2004

2. Outline: Welcome Welcome Aims Programme “E-Science Workflow Services” Background Structure Issues arising

3. Aims Report on current work Find areas of agreement Identify open issues Look for opportunities Research Development Collaboration Outcomes Sample workflows CP&E special issue Input to RGs and WGs ?

4. Morning Session Talks Architecture Applications Possible panel topics: Scripting Security Debugging Constraint Modelling Discussion Issues

5. Afternoon session Talks Languages (esp. BPEL) Tools & Enactment Possible panel topics: Adaptive enactment Workflow inference Event-driven enactment Incorporating devices & human input Discussion Outcomes

6. Outline: “E-Science Workflow Services” Welcome Aims Programme “E-Science Workflow Services” Background Structure Issues arising

7. E-Science Workflow Services > 90 participants Industry UK e-Science International e-Science Organisers Dave Berry (NeSC) Savas Parastatidis (NEReSC) Written report In progress UK e-Science Series December 3-5, 2003

8. e-Science Institute

9. Speakers Industry WfMC, WS-Choreography UK e-Science MyGrid/Taverna, GeoDise, DiscoveryNet, DAME, ICENI, Planning, RealityGrid, JIGSA, OGSA-DAI, Triana, AstroGrid International e-Science Chimera/Pegasus, BIRN, Kepler/Ptolemy, Thetis, Narada Research Operational research, Workflow and VO’s

10. Breakout Sessions Scientific Workflow Requirements Carole Goble Protocols in Scientific Workflows John Brooke Workflow Languages and Engines Matthew Addis

11. User requirements Reflect the modelling paradigm of the scientist. Varies between experiments, disciplines Which user would that be? Creators, users, auditors, validators (I know if its right when I see it) Biologists cf. bioinformaticians, and transitioning between Different users, different environments Appropriate levels of abstraction. User models -> workflow models Simple to use & intuitive creation, deployment, execution and debugging environments

12. A Scientist Writes… “Work in my problem solving environment so that I don’t need to change the way I work.”

13. Scientific Workflow lifecycles Incrementally exploratory prototypes Got the data, now get the Nature paper before the next guy Large scale production Got the idea, now get the data for many experiments, teams, communities Migration from one to the other Capture of prototype for later non-interactive replay in a parameterised fashion Different parts of the lifecycle May use different environments and policies Different sorts of users will interact

14. User interaction Creation & Discovery By example, plagiarism, drag and drop Collaborative multi-user interaction in creation Reusing workflows -> modularisation Reusing workflows with different parameters and data Composing workflows from different areas, disciplines and scales “eXtreme team workflow creation” Single User interaction with workflow execution Choice between paths of execution in specific states Parameter modification mid-run Collaborative multi-user interaction during execution?

15. Characterising Scientific Workflows Very large amounts of data Files, streams, database queries GridFTP, http, ftp, sockets … Sometimes it's the computation that needs to be moved to the data Data model and types Metadata Provenance Drivers Scientific questions, outcomes and vanity More creators than users in science?

16. Enactment Stack

17. Workflow vs. Service “Perform” document Transformed result of query 2 Stored procedure Result of query 1 OGSA-DAI Service

18. Questions? daveb@nesc.ac.uk Presentations from the workshop: http://www.nesc.ac.uk/action/esi/ contribution.cfm?Title=303