Collaborative eScience: Evolving Approaches

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Presentation transcript:

Collaborative eScience: Evolving Approaches Charles Severance NCeSS eCollaboration Workshop June 28, 2006 This material is Copyright Creative Commons Attribution 2.5

Outline A look back at the past 15 years Putting the “collab” in Collaborative eScience The current tools of Collaborative eScience Collaboration Portals Repository Some Refections A “future” eScience Case Study Some sections are hidden to fit into 30 minutes…

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

Over 15 Years of Collaborative eScience 2000 1991 - 1999 2001 2002 2003 2004 2005 2006 2007 SCIGate ? OGCE Grid Portal NEESGrid NEESIT Worktools CHEF Sakai Upper Atmosphere Research Collaboratory Space Physics Aeronomy Research Collaboratory Globus Tool Kit UARC/SPARC

What was SPARC? Before UARC.. SPARC - Space and Aronomy Research

What was SPARC? UARC/ SPARC SPARC - Space and Aronomy Research

SPARC 2/2001 600 users 800 data sources An advanced users view of SPARC; lots of information in multiple SPARC pages! The typical SPARC collaboration features down the left hand side, page information, resource list, active user list, and chat Streaming video in the lower left. Lots of data and model views throughout the rest of the screen shot. 2/2001 600 users 800 data sources

SPARC Software (1991-2001) Written from scratch No Middleware No Portal Technology Three rewrites over 10 years NextStep (Version 1) Java Applets with server support (Version 2) Browser based - kind of like a portal (Version 3) At the end, in 2001 - it was ready for another rewrite

Keys to SPARC Success Ten years of solid funding Team consistency Long enough to make and recover 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 Funding ended in 2001 - Project continued on inertia for a few years - but then faded away as its technology became dated wnd there was no further investment in “the little things” (I.e. no one to call and ask questions of).

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 This is a bit of the UM perspective.

NEESGrid

Sakai CHEF and NEES - hidden Cut to the “chase” - eliminate the intermediate steps…

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

Sakai: Product Placement Teaching and Learning Sakai is a Collaborative Learning Environment. Sakai can be applied in both a teaching and learning environment as well as research collaboration. Collaboration and eResearch

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

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

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

… … Integration and Administration Applications and Users Atlas ITER CMS Configure: Atlas Portal Experiment Process Control Knowledge Store Sakai SRB Opal Clarens Metadata Gateway Technologies Portal Gateway Desktop Gateway … Management Components Control Experiment Simulation Process KnowledgeStore … … Configure: ITER Portal Experiment Process Control Knowledge Store Sakai SRB Opal Clarens Metadata Services and Components Identity Security MetaData SRB Sakai Opal Clarens Globus BlueGene ORNL … SciGate Production Petascale Compute Petascale Data Resources

Some Reflections

Some Reflections Computer Scientists like to “stay in their box” Many of the technology solutions work well in their “initial domain” Once an eScience team “adopts” a technology (often step 1) their further progress (and focus) is limited by the technology. Agility is very important in the early phases of eScience Builders of components *must* make their component as interoperable as possible

Sakai and Web 2.0 Web 2.0 is about making sure data is available in some form beyond just displayed in the Sakai Tool Set. Formats RSS Resource Description Framework (RDF) HTML Protocols RSS / getData / SOAP / REST Consuming Applications Portals Google delic.io.us

Sakai Data Interoperability Portal Environment Personal Environment LMS Systems Collaboaration Environment Authoring Environment Content Management One word people use for this is an “ecology”. An rich, nurturing supportive environment where stuff “grows”. Mention SakaiBrary. Identity ACL Data Repository ... interoperability and data portability are key elements...

Sakai “Swiss Army Knife” HTML HTML CalDav RSS Collaboration and Learning Collaboration and Learning WebDav SOAP WebDav SOAP To allow Sakai to be used as a component as much as possible, we need to implement any and every protocol that deployers will need to integrate Sakai into any environment. SPARQL iCal Current Sakai REST Future Sakai

Interoperability at the UI RSS, ATOM, RDF, SOAP, REST, HTML Interoperability at the UI The SOAUI Higher Education Academy (HEAcademy) Tomorrow at Cambridge - Keynote.

Reflecting on Middleware and Virtual Organizations (slides not hidden)

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

Is Middleware The Universal Connector? Portal Technology Collaborative Tools Data Sources Middleware Identity ACL Knowledge Tools Shared Compute I call this model: “middleware in the middle”. Effectively nothing happens unless the middleware is involved. The middleware’s performance, complexity, reliability, and ease of use become the rate-limiting factor… To meet this need, middleware must be very fast, very simple, very easy to understand, very stable (I.e. not change so much) work with many different languages. Middleware must stand the test of time in production environments… Data Repository

The Universal Connectors Portal Technology Collaborative Tools Data Sources tcp/ip http/https Identity ACL web services Knowledge Tools Shared Compute If we want cross-language, cross environment, there are actually very few technologies which reach the “universal connector” status. Data Repository

Is Middleware “inside” each application? Portal Technology Data Sources Collaborative Tools There is no middleware that is sufficient to be a part of every single application ever written across all languages. So where is it? Identity ACL Data Repository Shared Compute Knowledge Tools

Middleware is simply another component - used as needed Portal Technology Data Sources Collaborative Tools Middleware Identity ACL Middleware is no better, worse, or even that much different than any of the other components. It is used by the other components *if and only if* those components find the services provided by the middleware useful. Middleware gets no special “must be used” status - if it adds value to an eScience application it will be used - if not - developers/integrators will find another way. Data Repository Shared Compute Knowledge Tools

Identity and Access Control: A very important function of Middleware Portal Technology Data Sources Collaborative Tools Middleware Lets Talk about This Identity ACL Middleware is no better, worse, or even that much different than any of the other components. It is used by the other components *if and only if* those components find the services provided by the middleware useful. Middleware gets no special “must be used” status - if it adds value to an eScience application it will be used - if not - developers/integrators will find another way. Data Repository Shared Compute Knowledge Tools

Chalk Talk:Identity and Access Control CAS ?? Shibboleth MyProxy GridShib Globus Identity ACL K.X509 Kerberos ??? LDAP What to do and who will fund it? For portals this took 5 years - once we set out to do it. Cosign PubCookie Convergence Competition Collaboration

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?

AUTHN/AUTHZ Meetings

My eScience Fantasy

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

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

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

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 - make sure the VO is easy to use! 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 The VO is the “first impression” that most users get of collaborative eScience. Those Computer Scienctists who are “security experts” are the least user-friendly of all computer scientists. Most folks deploying VO’s want to push the envelope on the very definition of “what is a VO” with no concern as to what the users really want or will tolerate.

Heart Study Collaboratory Login My Workspace A B C D E Open Forum Home Chat Resources Tutorials Site B Mail List Live Meetings All this step does is provide simple access to the work that is being done and give each one a tab. Let folks “learn about one another”. Keep it simple - First, do no harm.

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

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

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

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

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

D A E B F C Local Repo Central Repo GUID Service Identity ACL Local Experiments Compute E B Remote Observation GUID Service Data Models Vol 4 F Vol 3 Vol 2 The key of this sequence is how little each of the science activities has been affected so far. I understand this is a fantasy. But the underlying principle is the Hippocratic oath - “first, do no harm”. Vol 1 C Identity ACL Heart Study Collaboratory Login My Workspace A B C D E Open Forum Home Chat Resources Tutorials Site B Mail List Live Meetings eDocuments Local Repo Tutorials

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

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

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

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 cannot be “in the middle” because then it gets “in the way”) 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 Infrastructure is what is in the middle. There is a long way between middleware and infrastructure.

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”