1. Collaborative eScience: Evolving Approaches Charles Severance Rutgers CyberInfrastructure Meeting April 4, 2006 www.dr-chuck.com

2. Outline A look back at the past 15 years Putting the “collab” in Collaborative eScience The current tools of Collaborative eScience –Collaboration –Portals –Repository Reflecting on 15 years of Experience –What is wrong with Middleware? –Authorization and Authentication - Are we there yet? A “future” eScience Case Study

3. The Founding Concepts Scientific Domain Groups of People Common User Interface Data Sharing –In the moment –Long-term Experimental Equipment Compute Visualization

4. Over 15 Years of Collaborative eScience 20001991 - 19992001200220032004200520062007 UARC/SPARC SakaiWorktoolsCHEF OGCE Grid Portal NEESGrid Globus Tool Kit NEESIT SCIGate ?

5. What was SPARC? Before UARC..

6. What was SPARC? UARC/ SPARC

7. 2/2001 600 users 800 data sources

8. SPARC Software Written from scratch –No Middleware –No Portal Technology Three rewrites over 10 years –NextStep –Java Applets with server support –Browser based - kind of like a portal At the end, in 2001 - it was ready for another rewrite

9. Keys to SPARC Success Ten years of solid funding –Team consistency –Long enough to learn from “mistakes” Long term relationship between IT folks and scientists - evolved over time - relationship was “grey” Software rewritten several times over life of project based on evolving user needs and experience with each version of the program Portion of effort was invested in evaluation of usability - feedback to developers

10. After SPARC: Now What? Getting people together is an important part of collaborative eScience –WorkTools - Based on Lotus Notes –CHEF - Collaborative framework - Based on Java and Jetspeed –Sakai - Collaboration and Learning Environment - Java Critical point: Collaborative software is only one component of eScience UM Focus: Building reusable user interface technologies for the people part of collaborative eScience

11. WorkTools Over 9000 users (2000 active) at the end of 2003 WorkTools - The “organic” single-server approach - if you build it (and give away free acounts), they will come…

12. CompreHensive CollaborativE Framework (CHEF) Fall 2001: CHEF Development begins –Generalized extensible framework for building collaboratories Funded internally at UM All JAVA - Open Source –Jakarta Jetspeed Portal –Jakarta Tomcat Servlet Container –Jakarta Turbine Service Container Build community of developers through workshops and outreach

13. CHEF Applications CourseTools Next Generation WorkTools Next Generation NEESGrid NSF National Middleware Grid Portal

14. NEESGrid - The Equipment Network for Earthquake Engineering Simulation NSF Funded. NCSA, ANL, USC/ISI, UM, USC, Berkeley, MSU

15. CHEF-Based NEESGrid Software

16. Overall Data Modeling Efforts NEES Site ASite CSite B Equipment People Experiments Trials EquipmentPeople ExperimentsTrials Data Tsnumai Specimen Shake Table Specimen Geotech Specimen Centrifuge Specimen UnitsSensors Descriptions Site Specifications Database Project Description Domain Specific models Common Elements Data / Observations

17. DT Main System PTZ/ USB Still Capture DT Client BT848 Video Frames DT Client Capturing Video and Data Camera Control Gateway DAQ Data Capture DT Client Simulation Coordinator Site A Site B

18. DT Main System Data Monitoring Tools Still Image / Camera Control ~ <> ^ ^ <> Camera Control Gateway Creare viewers Still image camera control Thumb- nail

19. What’s in a name? Sakai is named after Hiroyuki Sakai of the Food Channel Television program “Iron Chef”. Hiroyuki is renowned for his fusion of French and Japanese cuisine.

20. Sakai General Collaborative Tools Announcements Assignments Chat Room Threaded Discussion Drop Box Email Archive Message Of The Day News/RSS Preferences Resources Schedule Web Content Worksite Setup WebDAV

21. Requirements Overlap Physics Research Collaboration Earthquake Research Collaboration Teaching and Learning Grid Computing Visualization Data Repository Large Data Libraries Quizzes Grading Tools Syllabus SCORM Chat Discussion Resources

22. Sakai: Product Placement Collaboration and eResearch Teaching and Learning

23. Additional General Collaboration Tools Under Development Wiki based on Radeox Blog Shared Display Shared Whiteboard Multicast Audio Multicast Video These are works-in-progress by members of the Sakai eResearch community. There are no dates for release.

24. NMI / OGCE www.ogce.org NSF National Middleware Initiative Indiana, UTexas, ANL, UM, NCSA

25. Chalk Talk:School of Portals (2004) OGCE 1.1 XCAT NEES 3.0 GridPort NEES 1.1 GridPort 3 Sakai uPortal CHEF OGCE 1.2 ? OGCE 2 Jetspeed Alliance GridPort 2 CompetitionCollaboration Convergence GridSphere

26. Chalk Talk:School of eScience Portals (2006) OGCE 1.1 XCAT GridPort NEES 1.1 GridPort 3 Sakai uPortal CHEF OGCE 2 Jetspeed Alliance GridPort 2 CompetitionCollaboration Convergence GridSphere SciGate ? SciDoc

27. Atlas Portal GatewayDesktop Gateway Applications and Users ITERCMS Gateway Technologies Services and Components Resources SRB Petascale Compute ClarensIdentity Security OpalMetaData Petascale Data SciGate Production Integration and Administration Sakai Globus BlueGeneORNL… Management Components Control ExperimentSimulation KnowledgeStore … Process … … Configure: Atlas Portal Experiment Process Control Knowledge Store Sakai SRB Opal Clarens Metadata Configure: ITER Portal Experiment Process Control Knowledge Store Sakai SRB Opal Clarens Metadata

28. The Ecology of Collaborative eScience

29. Collaborative Tools Shared Compute Data Sources Data Repository Portal Technology Knowledge Tools Scope of Collaborative E-Science “..composing and orchestrating many technologies…” “..interoperability is key…” Identity ACL

30. User Interface for Collaborative E- Science Portals are an excellent technology for building a federated user interface across these disparate components using standards like JSR-168. Collaborative Tools Shared Compute Data Sources Data Repository Portal Technology Knowledge Tools Identity ACL

31. Portals may only be an intermediate step in the process.. Collaborative Tools Shared Compute Data Sources Data Repository Portal Technology Knowledge Tools Identity ACL Desktop Applications

32. Focus of Sakai Activity in eScience Sakai is focused primarily on integration with portals and working closely with data repositories. Collaborative Tools Shared Compute Data Sources Data Repository Portal Technology Knowledge Tools Identity ACL Discuss First

33. Collaboration.vs. Portal Basic organization is about the thing it represents - Teragrid, NVO Site customization is based on the resource owners Sometimes there is an individual customization aspect Many small rectangles to provide a great deal of information on a single screen Portals think of rectangles operating independently - like windows Think “Dashboard” Basic organization is about the shape of the people and groups Customization based on the “group leaders” New groups form quickly and organically Doing one thing at a time - chat, upload - perhaps multiple active windows on a desktop Very interactive Think of navigation as picking a tool or switching from one class to another Think “Application”

34. Sakai Portlet Version 0.2 Tree View Gallery View Proxy portlets Source in SVN Configurable via properties file Announcements (sakai.announcements) Assignments (sakai.assignment) Chat Room (sakai.chat) Discussion (sakai.discussion) Gradebook (sakai.gradebook.tool) Email Archive (sakai.mailbox) Membership (sakai.membership) Message Forums (sakai.messageforums) Preferences Tool (sakai.preferences) Presentation (sakai.presentation) Profile (sakai.profile) Resources (sakai.resources) Wiki (sakai.rwiki) Tests & Quizzes (sakai.samigo) Roster (sakai.site.roster) Schedule (sakai.schedule) Site Info (sakai.siteinfo) Syllabus (sakai.syllabus)

35. Sakai JSR-168 Portlet Web Services are used to login to Sakai establish a session and retrieve a list of Sakai Sites, Pages, and Tools The portlet is 100% stock JSR-168 –Works in Pluto, uPortal, and GridSphere

36. Three Variations Display the Sakai gallery - all of Sakai except for the login and branding. Retrieve the hierarchy of sites, pages and tools display in a tree view with the portlet and show selected tools/pages in an iframe within the portlet Proxy tool placement for a particular Sakai tool such as sakai.preferences

37. Sakai Gallery View

38. How Gallery Works uPortal, Pluto, or GridSphere Sakai Web Svcs Charon Portal Sakai Portlet Login /portal/gallery

39. Sakai Tree View

40. How Tree View Works uPortal, Pluto, or GridSphere Sakai Web Svcs Charon Portal Sakai Portlet Login ToolList /portal/page/FF96

41. Sakai Proxy Tool

42. Proxy Tool Selection

43. How Proxy Portlet Works uPortal, Pluto, or GridSphere Sakai Web Svcs Charon Portal Sakai Portlet Login SiteList /portal/page/FF96 1 2

44. SakaiSite.getToolsDom http://localhost:8080/portal http://localhost:8080 http://localhost:8080/gallery My Workspace ~csev http://localhost:8080/portal/worksite/~csev af54f077-42d8-4922-80e3-59c158af2a9a Home http://localhost:8080/portal/page/af54f077-42d8-4922-80e3-59c158af2a9a b7b19ad1-9053-4826-00f0-3a964cd20f77 Message of the Day sakai.motd http://localhost:8080/portal/tool/b7b19ad1-9053-4826-00f0-3a964cd20f77 85971b6b-e74e-40eb-80cb-93058368813c My Workspace Information sakai.iframe.myworkspace http://localhost:8080/portal/tool/85971b6b-e74e-40eb-80cb-93058368813c New WS method is upwards compatible with getSitesDom

45. Sakai Repository Integration Approach

46. Focus of Sakai Activity in eScience Sakai is focused primarily on integration with portals and working closely with data repositories. Collaborative Tools Shared Compute Data Sources Data Repository Portal Technology Knowledge Tools Identity ACL D i s c u s s N o w

47. Collaboration.vs. Repository Many different systems may be active at the same time Systems evolve, improve, and are often replaced every few years Systems focused on the dynamic needs of users and applications Thousands of simultaneous online users Performance tuning Must be very easy to use; almost unnoticeable Used informally hundreds of times per day per user Think “E-Mail” Generally one system for the area Long term strategic choice for institution System focused on accessing, indexing, curation, and storage Millions of high quality objects properly indexed Data and metadata quality Must enforce standards and workflow to insure data quality Most use is very purposeful: search, publish, add value Think “Library”

48. Inbound Object Flow Ingest Create and use in native form Prepare for storage Data Model Store Curate, convert, update and maintain over time IndexLens Search View Reuse DRSakai The DR establishes a data model for “site” objects. The CLE hands sites to the DR. The DR may have to do “model” or content cleanup before completing the ingest process. The lens or disseminator understands the data model and is capable of rendering the objects. The lens is part of the DR. Preparation for storage may include cleanup, conversion, copyright clearance, and other workflow steps.

49. Outbound Object Flow Data Model IndexLens Search View Reuse DR Sakai Sakai can find and re-use objects in the repository. Data Model Lens ViewSearch Reuse

50. Sakai and Repositories Going Forward Instead of solving the problem by creating a single DR technology that is a superset - which might take years Focus on data portability between systems - reduce the impedance mismatch (or needed conversion between systems) RDF enables object portability across systems, languages, and technologies

51. Sakai Repository Approach Move Sakai and other Collaboration systems toward RDF –Experiment with using RDF as native storage format –High Performance RDF - Fedora testing - 180M tuples - complex queries - 70ms Move data repositories toward RDF –Move from schema-based stovepipe objects to OWL/RDF based objects with referential integrity –Explore dimensions of portability of disseminator / lenses - this is an important research area Get started immediately….

52. Fedora Images

53. Some Reflections

54. Collaborative Tools Shared Compute Data Sources Data Repository Portal Technology Knowledge Tools Where is the Middleware? “..composing and orchestrating many technologies…” “..interoperability is key…” Identity ACL

55. Middleware Collaborative Tools Shared ComputeData Sources Data RepositoryPortal Technology Knowledge Tools Identity ACL Is Middleware The Universal Connector?

56. Collaborative Tools Shared ComputeData Sources Data RepositoryPortal Technology Knowledge Tools Identity ACL The Universal Connectors tcp/iphttp/https web services

57. Collaborative Tools Shared Compute Data Sources Data Repository Portal Technology Knowledge Tools Is Middleware “inside” each application? Identity ACL

58. Collaborative Tools Shared Compute Data Sources Data Repository Portal Technology Knowledge Tools Middleware is simply another component - used as needed Middleware Identity ACL

59. Collaborative Tools Shared Compute Data Sources Data Repository Portal Technology Knowledge Tools Identity and Access Control: A very important function of Middleware Middleware Identity ACL Lets Talk about This

60. Chalk Talk:Identity and Access Control CAS Shibboleth Kerberos Globus CompetitionCollaboration Convergence LDAP PubCookie K.X509 MyProxy ?? GridShib Cosign ??? Identity ACL

61. Identity and ACL: Goal State One server - one software distribution Virtual Organization Software Supports all protocols –Globus Certificate Authority –Shibboleth –LDAP –MyProxy –Kerberos Who will do this? Who will fund this? Who can get these competitors to cooperate?

62. AUTHN/AUTHZ Meetings

63. My eScience Fantasy

64. The pre-requisites My net worth is $5B (I give myself grants) I encounter some tech-savvy scientists in a field who are using technology to do world-class research… They have never been visited by any other computer scientist… They are working in groups of 1-30 geographically distributed around the world They all work on a beach with Internet2 connections and wide-open wireless and favourable exchange rates

65. A B D E Vol 4 Vol 3 Vol 2 Vol 1 F C Compute Data Models Tutorials Experiments Remote Observation eDocuments

66. Step 1: Visit The Scientists Understand what they are doing and how they are doing it? Ask them how they would like to improve it. Show each application to other scientists. Ask the other scientists how they would improve it. Help each group improve their work - help them using whatever technology they are currently using

67. Step 2: Add some technology Install the super-multi-protocol Virtual Organization software and provide a NOC for the VO software - identity and simple attributes Install Sakai - point it at the VO software for identity add icon at the top of Sakai Give each scientist an account in the VO Give each effort in the field a site within Sakai

68. Heart Study Collaboratory Login My WorkspaceABCDEOpen Forum Home Chat Resources Tutorials Site B Mail List Live Meetings

69. Step 2: Use the VO For those who want to protect their information, help them add SSO to their sites, backed by the VO service Since it is multi-protocol - likely there will be no modification of the underlying science code - only a server configuration change Identity ACL

70. A B D E Vol 4 Vol 3 Vol 2 Vol 1 F C Compute Data Models Tutorials Experiments Remote Observation eDocuments Identity ACL Heart Study Collaboratory Login My WorkspaceABCDEOpen Forum Home Chat Resources Tutorials Site B Mail List Live Meetings

71. Step 4: Unique Identifier Service Come up with a way for any member of the VO to “get” a unique identifier Demand some information (build a little data model) –Person’s name and organization (implicit from request) –What kind of thing this will represent (experiment, document, image series) –Simple description –Keyword/value extensions Build an simple way request and retrieve these through a simple web service - capture implicit metadata from request (when, IP address, etc). Make sure it works from perl! Encourage community to start marking “stuff” with these identifiers in their stovepipes

72. Step 5: Data Models Begin to work with subsets of the field to try to find common data models across stovepipes Start simple - use very simple RDF - human readable Broaden / deepen model slowly - explore variations Define simple file-system pattern for storing metadata associated with a file and/or a directory

73. Step 6: A Backup-Style Repo Build a data repository which will function as a backup Basic idea - each time you get identifier - this enables backup space - any data and/or metadata can be uploaded under that particular identifier and left in the repository Make the repo multi-protocol, FTP, DAV, Web-Service with attachments, GridFTP, etc. Make it so there can be a network of cooperating repositories

74. A B D E Vol 4 Vol 3 Vol 2 Vol 1 F C Compute Data Models Tutorials Experiments Remote Observation eDocuments Identity ACL Heart Study Collaboratory Login My WorkspaceABCDEOpen Forum Home Chat Resources Tutorials Site B Mail List Live Meetings GUID Service Central Repo Local Repo Local Repo

75. Year 4 and on… Once the basic stovepipes have been “brought in from the cold” and made part of a community with no harm, the next steps are to begin to work “cross- stovepipe” –Evolve data models to be far richer with many variants –Build value added tools that are aware of the data models and are usable across stovepipes Teach the community to build and share tools - gently encourage development standards - Java / JSR-168 perhaps Most important: Always listen to the users

76. Science at the center of eScience Connect Enhance Data Models Data Storage New Tools New Approaches Priority Science Scientists … start at the center and work outwards… … apply technology when the users will see it as a “win” … Communicate New Technologies Repositories

77. Conclusion Many years ago, eScience had science as its main focus Custom approaches resulted in too many unique solutions Computer scientists began a search for the “magic bullet” - each group found a different magic bullet Each group now competes for mind share (and funding) to be the “one true” magic bullet

78. Conclusion (cont) One way to solve the “many competing technologies” solution is to form “super groups” which unify the technologies No single technology gets to claim “they are the one” (Middleware is not “in the middle”) Each technology needs to become a drop-in service/component which is available for use only when appropriate Once we can get past looking at the technologies as the main focus, we get back to science as the main focus

79. Lets remember why we started this whole field in the first place… Scientific Domain Groups of People Common User Interface Data Sharing –In the moment –Long-term Experimental Equipment Compute Visualization To download www.dr-chuck.com “Chuck’s Talks”