1. August 31 2006, Elsevier, Amsterdam Scientific Workflows in e-Science Dr Zhiming Zhao (zhiming@science.uva.nl)zhiming@science.uva.nl System and Network Engineering, University of Amsterdam Virtual Laboratory for e-Science

2. August 31 2006, Elsevier, Amsterdam Outline Background Scientific workflow management system Virtual Laboratory for e-Science Our approach Challenges and research lines Activities

3. August 31 2006, Elsevier, Amsterdam Problem solving: a typical scenario in scientific research Analysis Hypothesis Related work Propose experiments Define steps Prototype computing systems Perform experiments Data collection Visualization Validation Adjust experiment Refine hypothesis Presentation Dissemination Define problemsExperiments Data analysis Discovery Activities are: -Iterative, dynamic, and human centered -Requires different levels of resources

4. August 31 2006, Elsevier, Amsterdam Example scenarios In problem analysis  Identify domains, search key problems, find typical methods, and review related work In scientific experiments: scientific computing & data processing  Define dependencies between computing and data processing tasks, and schedule their runtime behavior In data analysis  Visualization, compare the results of different parameters, keep meaningful configuration and continue experiments  Search related work, compare results In dissemination  Documenting experiments, present results, citation, publication

5. August 31 2006, Elsevier, Amsterdam Computer support for problem solving Problem Solving Environment: ( E Gallopoulos et. al., IEEE CS Eng. 1994 ) Organize different software components/ tools Allows a user to assemble these tools at a high level of abstraction Control runtime behavior of experiments Examples: MATLab, Ptolemy, etc. Traditional PSE: organize and execute resources locally! Distributed resources Distributed Parallel computing Visualization, Remote resource invocation Distributed data sharing & dissemination Scientific workflow management systems: A new guise of PSE!

6. August 31 2006, Elsevier, Amsterdam Inside a Scientific Workflow Management System In our view, a SWMS at least implements: A model for describing workflows; An engine for executing/managi ng workflows; Different levels of support for a user to compose, execute and control a workflow. Workflow (based on certain model) Engine User support resources Composition Engine level control Resource level control A SWMS

7. August 31 2006, Elsevier, Amsterdam Scientific Workflows in e-Science Workflows varies at different Phases of experiments: design, runtime control, dissemination; Abstractions of resources: concrete and abstract; Levels of activity details: computing, data access, search/matching, human activities; … Experiment processes Abstract workflows Executable (concrete workflows) Workflows for administration, e.g., AAA,and other issues.

8. August 31 2006, Elsevier, Amsterdam Diversity in SWMS Taverna: -Web services based language: Scufl; -FreeFluo: engine -Graphical viz of workflow Kepler: -Actor,director -MoML -Execution models Triana: -Components -Task graph -Data/control flow DAGMan: -Computing tasks -DAG Pegasus: -Based on DAGMan -VDL -DAG …

9. August 31 2006, Elsevier, Amsterdam Virtual Laboratory for e-Science Dutch telescience Data intensive science Medical diagnosis Generic e -science framework layer Application layer Bioinformatics ASP Bio diversity Food Informatics Grid layer

10. August 31 2006, Elsevier, Amsterdam Mission Effectively reuse existing workflow managements systems, and provide a generic e-Science framework for different application domains. A generic framework can Improve the reuse of workflow components and the workflows for different experiments Reduce the learning cost for different systems Allow application users to work on a consistent environment when underlying infrastructure changed

11. August 31 2006, Elsevier, Amsterdam Previous work: VLAM-G environment VLAM-G  A Grid enable PSE  Data intensive applications  Visual interface  Two levels of workflow support  Human interaction support

12. August 31 2006, Elsevier, Amsterdam Workflow in VLAMG

13. August 31 2006, Elsevier, Amsterdam Experiment Topology –Graphical representation of self-contained data processing modules attached to each other in a workflow. Process-Flow Template – Graphical representation of data elements and processing steps in an experimental procedure. Study – Descriptions of experimental steps represented as an instance of a PFT with references to experiment topologies. VLAM-G PFT/Study

14. August 31 2006, Elsevier, Amsterdam Lessons learned How to introduce a new PSE to a domain scientist?  Because it has a beautiful architecture?  Or because it can allow a scientist to keep their current work style? How to use existing work?  Scientists need one system or more options? How to include user in the computing loop?  Dynamic workflows and human in the loop computing are important. Z. Zhao et al., “Scientific workflow management: between generality and applicability”, QSIC 2005, Australia

15. August 31 2006, Elsevier, Amsterdam Workflow support in VL-e Recommend suitable workflow systems for different application domains:  Analyze typical application use cases  Define small projects with different application domains  Review existing workflow systems  Recommend four workflow systems: Triana, Taverna, Kepler, and VLAMG A long term  Extend VLAMG and develop our own generic workflow framework

16. August 31 2006, Elsevier, Amsterdam A workflow bus paradigm Workflow bus TavernaKepler Triana Sub workflow 1 Sub workflow 2 Sub workflow 3 Workflow A workflow bus is a special workflow system for executing meta workflows, in which sub workflows will be executed by different engines. Z. Zhao et al., “Workflow bus for e-Science”, in IEEE Int’l Conf. e-Science 2006, Amsterdam

17. August 31 2006, Elsevier, Amsterdam Applications of workflow bus Use case 1:  A user has workflow in Taverna  Some functionality is missing in Taverna but can be provided by Triana  He can develop the workflow in two systems, and run it via the workflow bus Use case 2:  A user wants to execute a Taverna or Triana workflow in multiple instances with different input data

18. August 31 2006, Elsevier, Amsterdam Ongoing research Web service in data intensive applications Execution models for Grid workflows Including PSE in scientific workflows Industrial standards in scientific workflows

19. August 31 2006, Elsevier, Amsterdam Relevance between our research and Elsevier’s work In a same context from the scale of entire lifecycle of e-Science experiments Different focuses  We focus on runtime behavior of scientific experiments, e.g., Grid computing, data/computing intensive applications, and scheduling of computing tasks  Elsevier highlights data search and integration on well structured data bases, research preparation, and literature search and management

20. August 31 2006, Elsevier, Amsterdam Cont. Different characteristics in workflows  In our workflows, processing and managing runtime dynamic data is the key patterns  In Elsevier workflows, storage, replicate, access, match and integrate static data might be more common Facing similar challenges:  Semantics based data search and integration  Workflow provenance  Collaborative interaction (workflow development, resource sharing, knowledge transfer)  Modeling user profiles

21. August 31 2006, Elsevier, Amsterdam Activities Int’l workshop on “Workflow systems in e-Science”, organized by Zhiming Zhao and Adam Belloum, in the context of ICCS06, Reading University, May 28, 2006.  Proceedings is in LNCS, Springer Verlag.  A special issue will be published in Scientific Programming Journal.  http://staff.science.uva.nl/~zhiming/iccs-wses http://staff.science.uva.nl/~zhiming/iccs-wses Workshop on “Scientific workflows and industrial workflow standards in e-Science ”, organized by Adam Belloum and Zhiming Zhao, in the context of IEEE e-Science and Grid computing conference in Amsterdam December 2006.  Pegasus, Dr. Ewa Deelman (Department of Computer Science University of South California)  BPEL, Dr. Dieter König (IBM Research Germany Development Laboratory)  Kepler, Dr. Bertram Ludäscher (Department of Computer Science University of California, Davis)  Taverna, Prof. Peter Rice (European Bioinformatics Institute)  WS and Semantic issues, Dr. Steve Ross-Talbot (CEO, and a co-founder, of Pi4 Technologies)Pi4 Technologies  Triana, Dr. Ian J. Taylor (Department of Computer Science Cardiff University)  http://staff.science.uva.nl/~adam/workshop/VL-e-workshop.htm http://staff.science.uva.nl/~adam/workshop/VL-e-workshop.htm

22. August 31 2006, Elsevier, Amsterdam References 1. Virtual Laboratory for e-Science: www.vl-e.nlwww.vl-e.nl 2. Network and System Engineering, Faculty of Science, University of Amsterdam: http://www.science.uva.nl/research/sne/ http://www.science.uva.nl/research/sne/ 3. Z. Zhao; A. Belloum; H. Yakali; P.M.A. Sloot and L.O. Hertzberger: Dynamic Workflow in a Grid Enabled Problem Solving Environment, in Proceedings of the 5th International Conference on Computer and Information Technology (CIT2005), pp. 339-345. IEEE Computer Society Press, Shanghai, China, September 2005. 4. Z. Zhao; A. Belloum; A. Wibisono; F. Terpstra; P.T. de Boer; P.M.A. Sloot and L.O. Hertzberger: Scientific workflow management: between generality and applicability, in Proceedings of the International Workshop on Grid and Peer-to-Peer based Workflows in conjunction with the 5th International Conference on Quality Software, pp. 357-364. IEEE Computer Society Press, Melbourne, Australia, September 19th-21st 2005. 5. Z. Zhao; A. Belloum; P.M.A. Sloot and L.O. Hertzberger: Agent technology and scientific workflow management in an e-Science environment, in Proceedings of the 17th IEEE International conference on Tools with Artificial Intelligence (ICTAI05), pp. 19-23. IEEE Computer Society Press, Hongkong, China, November 14th-16th 2005.