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Knowledge Environments for Science:
Representative Projects
Ian Foster
Argonne National Laboratory
University of Chicago
http://www.mcs.anl.gov/~foster
Symposium on Knowledge Environments for Science, November 26, 2002
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Comments Informed By Participation in …
E-science/Grid application projects, e.g.– Earth System Grid: environmental science
– GriPhyN, PPDG, EU DataGrid: physics
– NEESgrid: earthquake engineering Grid technology R&D projects
– Globus Project and the Globus Toolkit
– NSF Middleware Initiative Grid infrastructure deployment projects
– Alliance, TeraGrid, DOE Sci. Grid, NASA IPG
– Intl. Virtual Data Grid Laboratory (iVDGL) Global Grid Forum: community & standards
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Data Grids for High Energy Physics
Enable community to access & analyze petabytes of data
Coordinated intl projects– GriPhyN, PPDG, iVDGL, EU
DataGrid, DataTAG Challenging computer science
research Real deployments and
applications Defining analysis architecture for
LHC
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NEESgrid Earthquake Engineering Collaboratory
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Network for Earthquake Engineering Simulation
Field Equipment
Laboratory Equipment
Remote Users
Remote Users: (K-12 Faculty and Students)
High-Performance Network(s)
Instrumented Structures and Sites
Leading Edge Computation
Curated Data Repository
Laboratory Equipment (Faculty and Students)
Global Connections(fully developed
FY 2005 –FY 2014)
(Faculty, Students, Practitioners)
U.Nevada Reno
www.neesgrid.org
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Size distribution ofgalaxy clusters?
1
10
100
1000
10000
100000
1 10 100
Num
ber
of C
lust
ers
Number of Galaxies
Galaxy clustersize distribution
Chimera Virtual Data System+ GriPhyN Virtual Data Toolkit
+ iVDGL Data Grid (many CPUs)
Communities Need Not be Large:E.g., Astronomical Data Analysis
www.griphyn.org/chimera
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A “Knowledge Environment” is a System For …
“Interpersonalcollaboration”
“Integratingdata”
“Accessingspecializeddevices”
“Enablinglarge-scale
computation”
“Sharinginformation”
“Accessingservices”
“Largecommunities”
“Smallteams”
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It’s All of the Above: Enabling “Post-Internet Science”
Pre-Internet science– Theorize &/or experiment, in small teams
Post-Internet science– Construct and mine very large databases
– Develop computer simulations & analyses
– Access specialized devices remotely
– Exchange information within distributed multidisciplinary teams
Need to manage dynamic, distributed infrastructures, services, and applications
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Enabling Infrastructure for Knowledge Environments for Science
(aka “The Grid”)
“Resource sharing & coordinated problem solving in dynamic, multi-institutional virtual organizations”
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Grid Infrastructure What?
– Broadly deployed services in support of fundamental collaborative activities
– Services, software, and policies enabling on-demand access to critical resources
Open standards, software, infrastructure– Open Grid Services Architecture (GGF)
– Globus Toolkit (Globus Project: ANL, USC/ISI)
– NMI, iVDGL, TeraGrid Grid infrastructure R&D&ops is itself a distributed &
international community
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Lessons Learned (1)
Importance of standard infrastructure– Software: facilitate construction of systems, and
construction of interoperable systems
– Services: authentication, discovery, …, …
– Needs investment in research, development, deployment, operations, training
Building & operating infrastructure is hard– Challenging technical & policy issues
– Requisite skills not always available
– Can challenge existing organizations
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Lessons Learned (2)
Importance of community engagement– “Maine and Texas must have something to
communicate”
– Big science traditions seem to help
– Discipline champions certainly help
– Effective projects often true collaborations between disciplines and computer scientistis
Importance of international cooperation– Science is international, so is expertise
– Challenging, requires incentives & support
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Lessons Learned (3)
Collaborative science/Grids are a wonderful source of computer science problems– E.g., “virtual data grid” (GriPhyN): data,
programs, derivations as community resources
– E.g., security within virtual organizations Work in this space can be of intense interest
to industry– E.g., current rapid uptake of Grid
technologies
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