1. Using the VL-E Proof of Concept Environment Connecting Users to the e-Science Infrastructure David Groep, NIKHEF

2. Virtual Laboratory for e-Science (NL) To boost e-Science by –the creation of an e-Science environment –and doing research on methodologies To carry out concerted research –along the complete e-Science technology chain, –ranging from applications to networking, –focused on new methodologies and reusable components.

3. Grid Services Harness multi-domain distributed resources XXXXXXXX VL-e Application Oriented Services Food Informatics Dutch Telescience Medical Diagnosis & Imaging Bio- Informatics Data Intensive Science/ Bio- Diversity Virtual Laboratory for e-Science

4. VL-E in a nutshell Experiments become more complex –more than just coping with the data –Computer is integrated part of the experiment –support the experimental process end-to-end Technology (push) … Grid Resource Sharing Web Networks Application Needs (pull) Experiment validation Papers and associated data Provenance meta-data Information modeling Data/Resource Collection Access …

5. interpretation Rationalization of the experiment and processes via protocols processing processed data conversion, filtering, analyses, simulation, … experiment parameters/settings, algorithms, intermediate results, … Parameter settings, Callibrations, Protocols … software packages, algorithms … raw data acquisition sensors,amplifiers imaging devices,, … presentation visualization, animation interactive exploration, … Metadata Much of this is lost when an experiment is completed. The Experimental Process

6. Combining data sources Key element for all users: Data Combination From different organisations –data ownership preserved –data correctness maintained by preventing ‘forks’ Extracting common meaning –need for workflow definition and ontologies in collaborative experiments

7. Combining data in Cognition Science Collaborative scientific research –Information sharing –Metadata modeling Allows for experiment validation –Independent confirmation of results Statistical methodologies –Access to large collections of data and metadata Training –Train the next generation using peer reviewed publications and the associated data

8. Combining Acquisition and Simulation Robert: kun je hier een mooi plaatje voor maken? Het lijkt me de goede plaats om ook in- silico experimenten even te noemen

9. Role of the Proof-of-Concept (PoC) Platform for user application development Provisioning network & grid infrastructure –stable releases of common tools –tested ‘external’ middleware –stable releases of internal developments Support for users & dissemination –infrastructure installations –end-user helpdesk –on-site aid in migration

10. Application development NL-Grid production cluster Central mass-storage facilities + SURFnet Initial compute platform Stable, reliable, tested Cert. releases Grid MW & VL- software VL-e Proof of Concept Environment VL-e Rapid Prototyping Environment DAS-2, local resources VL-e Certification Environment NL-Grid Fabric Research Cluster Test & Cert. Grid MW & VL-software Compatibility Flexible, test environment Environments Usage Characteristics Virtual Lab. rapid prototyping (interactive simulation) Flexible, ‘unstable’ Download Repository PoC Installer Cluster Tools Developer CVS Nightly builds Unit tests stable, tested releases Integration tests Functionality tests Adventurous application people PoC Release n Release Candidate n+1 Developers Heaven/Haven Tagged Release Candidates LCG2.x + SRB + LCG2.x + others GT3.2 + * external middleware products Food Informatics Dutch Telescience Medical Diagnosis & Imaging Bio- Informatics Data Intensive Science/ Bio- Diver sity

11. Involving Users Training via tutorials on middleware –good attendance, but slow uptake later on On-site support in integration –good technology update, but people intensive User driven integration: application pull –rapid update, good attendance –requires an ICT scientist to work long-term with the domain scientists to recognize and extract generic elements

12. Tutorials Grid, LCG2 tutorials Hands-on event series ‘Grid Admin Nerd Group’ ‘After Sales Service’ Documentation User help-desk (by phone & mail) User Experience: nice, but information quickly ‘lost’

13. On-site support EMUTD example Maurice to provide image & input Effective use of EDG/EGEE tools for job submission, SRB for data access User experience: problem effectively solved! but with high manpower investment by PoC

14. Grid Services Harness multi-domain distributed resources Management of comm. & computing Management of comm. & computing Management of comm. & computing Potential Generic part Potential Generic part Potential Generic part Application Specific Part Application Specific Part Application Specific Part Virtual Laboratory Application Oriented Services Application pull Application Pull VL-E methodology

15. Can we keep our users content? Take care of grid & generic aspects –collaboration community building & security –policy-constraint & dynamic resource sharing Software Integration –there are many tools already … ‘just integrate them’ –but only wide deployment will show the weaknesses Make it work –consistent software engineering practices –hide changes lower layers by use of standard interfaces –Easy-to-use installers (PoC Installer, Quattor) –and teach us how to scale up to a grid service provider

16. http://www.vl-e.nl/