1. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN GriPhyN: Grid Physics Network and iVDGL: International Virtual Data Grid Laboratory

2. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Collaboratory Basics Two NSF-funded Grid projects in HENP (high energy and nuclear physics) and computer science –MPS and CISE have oversight GriPhyN and iVDGL are too closely related to discuss one without discussing the other –One is CS research and test application, the other is to build an international scale facility to do these tests, and to address other goals as well –Share vision, personnel and components These two collaboratories are part of a larger effort to develop the components and infrastructure for supporting data intensive science

3. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Some Science Drivers Computation is becoming an increasingly important tool of scientific discovery –Computationally intense analyses –Global collaborations –Large datasets The increasing importance of computation in science is more pronounced in some fields –Complex (e.g. climate modeling) and high volume (HEP) simulations –Detailed rendering (e.g. biomedical informatics) –Data intensive science (e.g. astronomy and physics) GriPhyN and iVDGL were founded to provide the models and software for the data management infrastructure for four large projects

4. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN SDSS / NVO SDSS / NVO are in full production Explore how the Grid can be used in astronomy –What’s the benefit? –How to integrate? –How can the Grid be used for future sky surveys? –Data processing pipelines are complex –Has made the most sophisticated use of the virtual data concept

5. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN LIGO Not in full production, but real data is being taken –LIGO I Engineering Runs 35 TB since 1999 and growing –LIGO I Science Runs 62 TB in two science runs, additional run planned that will generate 135 TB Eventual constant operation at 270 TB/year –LIGO II Upgrade Eventual Operation at 1-2 PB / year Need distributed computing power of the Grid Need virtual data catalogs for efficient dissemination of data and management of workflow

6. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN CMS / ATLAS CMS and ATLAS are two experiments being developed for the Large Hadron Collider at CERN Two projects, two cultures, but: –Similar data challenges –Similar geographic distribution –Moving closer to common tools through the LCG computing grid. Petabytes of data per year (100 PB by 2012)

7. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Function Types GriPhyN –Distributed Research Center iVDGL –Community Data System

8. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN GriPhyN

9. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN GriPhyN Funding Funded in 2000 through NSF ITR program $11.9M + $1.6M matching

10. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN GriPhyN Project Team Led by U. Florida and U. Chicago –PD’s Paul Avery (UF) and Ian Foster (UC) 22 Participant institutions –13 funded –9 unfunded Roughly 82 people involved 2/3 of activity computer science, 1/3 physics

11. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Funded Institutions –U. Florida –U. Chicago –CalTech –U. Wisconsin - Madison –USC / ISI –Indiana U. –Johns Hopkins U. –Texas A & M –UT Brownsville –UC Berkeley –U Wisconsin Milwaukee –SDSC Unfunded Institutions –Argonne NL –Fermi NAL –Brookhaven NL –UC San Diego –U. Pennsylvania –U. Illinois - Chicago –Stanford –Harvard –Boston U. –Lawrence Berkeley Lab

12. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Technology GriPhyN’s science drivers demand timely access to very large datasets and the computer cycles and information management infrastructure needed to manipulate and transform those datasets in a meaningful way Data Grids are an approach to data management and resource sharing in environments where datasets are very large –Policy-driven resource sharing, distributed storage, distributed computation, replication and provenance tracking GriPhyN and iVDGL aim to enable petascale virtual data grids

13. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Virtual Data Tools Request Planning & Scheduling Tools Request Execution & Management Tools Transforms Distributed resources (code, storage, CPUs, networks) Resource Management Services Security and Policy Services Other Grid Services Interactive User Tools Production Team Single Researcher Workgroups Raw data source  PetaOps  Petabytes  Performance Petascale Virtual DataGrids

14. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN GriPhyN Datagrid Contributions GriPhyN has three areas of contribution for achieving the DataGrid vision Contributing CS research –Virtual Data as a unifying concept –Planning, execution and performance monitoring –Integrating these facilities in a transparent and high-productivity manner: Making the grid as easy to use as a workstation and the web Disseminating this research through the Virtual Data Toolkit and other tools –Chimera –Pegasus Integrate CS research results into GriPhyN science projects GriPhyN experiments serve as an exciting but demanding CS and HCI laboratory

15. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Virtual Data Toolkit: VDT A suite of tools developed by the CS team to support science on the Grid Uniting theme is virtual data –Nearly all data in physics / astronomy is virtual data - derivations of a large, well known data set –It is possible to represent derived data as the set of instructions that created it –There is no need to always copy a derived data set - it can be recomputed if you have the workflow –Virtual data also has a number of beneficial side effects, e.g. data provenance,discovery, re-creation, workflow automation Many packages, a few are unique to GriPhyN, others are common across many Grid projects

16. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN TransformationDerivation Data created-by execution-of consumed-by/ generated-by “I’ve detected a calibration error in an instrument and want to know which derived data to recompute.” “I’ve come across some interesting data, but I need to understand the nature of the corrections applied when it was constructed before I can trust it for my purposes.” “I want to search an astronomical database for galaxies with certain characteristics. If a program that performs this analysis exists, I won’t have to write one from scratch.” “I want to apply an astronomical analysis program to millions of objects. If the results already exist, I’ll save weeks of computation.” Motivations Slide courtesy Ian Foster

17. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Chimera The Chimera Virtual Data System is one of the core tools of the GriPhyN Virtual Data Toolkit Virtual Data Catalog –Represents transformation procedures and derived data Virtual Data Language Interpreter –Translates user requests into Grid workflow

18. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Pegasus Planning Execution in Grids Tool for mapping complex workflows onto the Grid Converts abstract Chimera workflow into a concrete workflow, which is sent to DAGman for execution –DAGman is the Condor meta-scheduler –Determines sites and data transfers

19. Virtual Data Processing Tools VDLx abstract planner XML DAG Condor DAG Pegasus concrete planner Local shell planner shell DAG

20. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Galaxy cluster size distribution DAG Example: Sloan Galaxy Cluster Finder Sloan Data Jim Annis, Steve Kent, Vijay Sehkri, Fermilab, Michael Milligan, Yong Zhao, Chicago

21. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Galaxy cluster size distribution DAG Example: Sloan Galaxy Cluster Sloan Data With Jim Annis & Steve Kent, FNAL

22. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Resource Diagram

23. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN International Virtual Data Grid Laboratory: iVDGL

24. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Some Context There is much more to the DataGrid world than GriPhyN Broad problem space, with many cooperative projects –U.S. Particle Physics Data Grid (PPDG) GriPhyN –Europe DataTAG EU DataGrid –International iVDGL

25. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Background and Goals U.S. portion funded in 2001 as a Large ITR through NSF –$13.7M + $2M matching International partners responsible for own funding Aims of iVDGL –Establish a Global Grid Laboratory –Conduct DataGrid tests at scale –Promote interoperability –Promote testbeds for non-physics applications

26. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Relationship to GriPhyN Significant overlap –Common management, personnel overlap Roughly 80 people on each project, 120 total –Tight technical coordination VDT Outreach Testbeds –Common External Advisory Committee Different focus - domain challenges –GriPhyN - 2/3 CS, 1/3 Physics: IT Research –iVDGL - 1/3 CS, 2/3 Physics: Testbed deployment and operation

27. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Project Composition CS Research –U.S. iVDGL Institutions –UK e-science programme –DataTAG –EU DataGrid Testbeds –ATLAS / CMS –LIGO –National Virtual Observatory –SDSS

28. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN U FloridaCMS CaltechCMS, LIGO UC San DiegoCMS, CS Indiana UATLAS, iGOC Boston UATLAS U Wisconsin, MilwaukeeLIGO Penn StateLIGO Johns HopkinsSDSS, NVO U ChicagoCS U Southern CaliforniaCS U Wisconsin, MadisonCS Salish KootenaiOutreach, LIGO Hampton UOutreach, ATLAS U Texas, BrownsvilleOutreach, LIGO FermilabCMS, SDSS, NVO BrookhavenATLAS Argonne LabATLAS, CS iVDGL Institutions T2 / Software CS support T3 / Outreach T1 / Labs (not funded)

29. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN US-iVDGL Sites Partners?  EU  CERN  Brazil  Australia  Korea  Japan UF Wisconsin BNL Indiana Boston U SKC Brownsville Hampton PSU J. Hopkins Caltech Tier1 Tier2 Tier3 FIU FSU Arlington Michigan LBL Oklahoma Argonne Vanderbilt UCSD/SDSC NCSA Fermilab

30. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Component Projects iVDGL contains several core projects –iGOC International Grid Operations Center –GLUE Grid Laboratory Uniform Environment –WorldGrid – 2002 international demo –Grid3 – 2003 deployment effort

31. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN iGOC International Grid Operations Center –iVDGL “headquarters” –Analogous to a Network Operations Center –Located at Indiana University Single point of contact for iVDGL operations Database of contact information Centralized information about storage, network and compute resources Directory for monitoring services at iVDGL sites

32. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN GLUE Grid Laboratory Uniform Environment –A grid interface subset specification that permits applications to run on grids from VDT and EDG sources Effort to ensure interoperability across numerous physics grid projects –GriPhyN, iVDGL, PPDG –EU DataGrid, DataTAG, CrossGrid, etc. Interoperability effort focuses on: –Software –Configuration –Documentation –Test suites

33. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN WorldGrid Effort at a world wide DataGrid Easy to deploy and administer –Middleware based on VDT –Chimera development –Scalability Demo at SC2002 –United DataTAG and iVDGL

34. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Resource Diagram

35. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN iVDGL Management Project Coordination Group US External Advisory Committee GLUE Interoperability Team Collaborating Grid Projects TeraGridEDGAsiaDataTAG BTEV LCG? BioALICEGeo? D0PDCCMS HI ? US Project Directors Outreach Team Core Software Team Facilities Team Operations Team Applications Team International Piece US Project Steering Group U.S. Piece GriPhyN Mike Wilde

36. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Issues across projects Technical readiness Infrastructure readiness Collaboration readiness Common ground Coupling of tasks Incentives

37. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Technical readiness Very high Physics and CS are both very high on the adoption curve, generally Long history of infrastructure development to support national and global experiments

38. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Infrastructure readiness Also quite high Not all of the pieces are in place to meet demand The expertise exists within these communities to build and maintain the necessary infrastructure –Community is inventing the infrastructure Real understanding in the project that interoperability and standards are part of infrastructure

39. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Collaboration readiness Again, quite high Physicists have a long history of large scale collaboration CS collaborations built on old relationships with long time collaborators

40. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Common ground Perhaps a bit too high What you can do with a physics background: –Win the ACM Turing Award –Co-invent the World Wide Web –Direct the development of the Abilene backbone Because application community has a strong understanding of the required work and the technical aspects of the work, some friction about how work separates –History of physicists building computational tools e.g. ROOT

41. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Coupling of tasks Tasks decompose into subtasks that are somewhat tightly coupled –Locate tightly coupled tasks at individual sites

42. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Incentives Both groups are well motivated, but for different reasons CS is engaging in extremely cutting edge research across a large range of activities –Funded for deployment as well as development Physics is structurally committed to global collaborations

43. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Some successes Lessons in infrastructure development Outreach and engagement Community buy-in / investment Achieving the CS research goals for Virtual Data and Grid execution

44. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Infrastructure Dev Looking at the history of the Grid (electrical, not computational) –Long phases Invention Initial production use Adaptation Standardization / regulation –Geographically bounded dominant design I.e. 220 vs. 110

45. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Infrastructure Dev We don’t see this with GriPhyN / iVDGL –Projects concurrent, not consecutive –Pipeline approach to phases of infrastructure development –Real efforts at cooperation with other DataGrid communities Why? –Deep understanding at high levels of project that building it alone is not enough –Directive and funding from NSF to do deployment

46. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Outreach The GriPhyN / iVDGL community is extremely active in outreach to other projects and communities –Evangelizing virtual data –Distributed tools This is a huge win for building CI that others can use

47. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Community buy-in Together, these projects are funded at nearly $30M over 6 years This does not represent the total investment that was needed to make this work –Leveraged FTE –Unfunded testbed sites –International partners –Lots of collaboration with PPDG; starting some with Alliance Expeditions, etc This kind of community commitment necessary for a project of this size to succeed

48. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Challenges Staying relevant Building infrastructure with term limited funding

49. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Staying Relevant (1) The application communities are fast paced, high power groups of people –Real danger in those communities developing tools that satisfice while they wait for the tools that are optimal and fit into a greater cyberinfrastructure –Each experiment ideally wants tools perfectly tailored to their needs Maintaining user engagement and meeting the needs of each community is critical, but difficult

50. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Staying Relevant (2) In addition to staying relevant to the experiments, GriPhyN must also be relevant to the greater scientific community –To CS researchers –To similarly data-intensive projects Easy to understand code, concepts, APIs, etc. How do you accommodate both a focused client community and the broader scientific community Common challenge across many CI initiatives

51. SCHOOL OF INFORMATION UNIVERSITY OF MICHIGAN Limited Term Investment These projects are both funded under the NSF ITR mechanism –5 year limit Would you buy your telephone service from a company that was going to shut down after 5 years? Challenge to find a sustainable support mechanism for CI