The Grid as Future Scientific Infrastructure Ian Foster Argonne National Laboratory University of...
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The Grid as Future Scientific InfrastructureIan Foster
Argonne National LaboratoryUniversity of ChicagoGlobus Alliance
www.mcs.anl.gov/~foster
Session Title
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OverviewEvolution of scientific infrastructureTechnology drivers & new requirementsBuilding cyberinfrastructureStandards, software, and facilitiesExamplesFuturesVirtual dataDiscipline-specific cyberinfrastructuresChallenges
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Technology DriversInternet revolution: 100M+ hostsCollaboration & sharing the normUniversal Moores law: x103/10 yrsSensors as well as computersPetascale data tsunamiGating step is analysis& our old infrastructure?
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If We Cant Do it All in our Lab Then we need to be able to reach out into the network to obtain needed resourcesClusters, archives, instrumentationAnd integrate those resources & services into our data processing workflowsOperating at scale (not Web browsing)Which means we needCommunity facilitiesStandard means of accessing those facilitiesMeans of coordinating across facilitiesNew methodologies and tools
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Scale Metrics: Participants, Data, Tasks, Performance, Interactions,
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CyberinfrastructureAKA GridCommunitiesOperational procedures,
ServicesAuthentication, discovery,
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OverviewEvolution of scientific infrastructureTechnology drivers & new requirementsBuilding cyberinfrastructureStandards, software, and facilitiesExamplesFuturesVirtual dataDiscipline-specific cyberinfrastructuresChallenges
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Building CyberinfrastructureOpen standards: absolutely vitalDefine how we describe, discover, access, secure, monitor, & manage servicesE.g., Open Grid Services ArchitectureOpen facilities: enabled by standardsStandards-based access, community services (authentication, registry), operational supportE.g., TeraGrid, Grid3, NEESgrid, ESG, DOE Science Grid, LHC Computing Grid, NASA IPGOpen software: accelerate standards adoption Accelerates creation of facilities & applicationsGlobus Toolkit, NSF Middleware Initiative, etc.
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The Emergence ofOpen Grid StandardsIncreased functionality,standardizationCustomsolutions19901995200020052010
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Grid-Web Services Convergence
Web Services Messaging, Security, Etc.Open Grid Services InfrastructureDomain-Specific ServicesCore ServicesProgramExecutionData Services
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Grid-Web Services Convergence Completed: A Major Milestone!Web Services Messaging, Security, Etc.Open Grid Services InfrastructureDomain-Specific ServicesCore ServicesProgramExecutionData ServicesWS-Resource Framework
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Deploying Cyberinfrastructure
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NEESgrid Earthquake Engineering CollaboratoryU.Nevada Renowww.neesgrid.org
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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)
12/06/01 MRE Panel
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NEESgridMultisite OnlineSimulation Test(July 2003)IllinoisColoradoIllinois (simulation)W
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Earth System Grid (ESG) Goal: address technical obstacles to the sharing & analysis of high-volume data from advanced earth system models
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Grid2003: Towards a Persistent U.S. Open Science GridStatus on 11/19/03(http://www.ivdgl.org/grid2003)P
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Virtual ObservatoriesNo. & sizes of data sets as of mid-2002, grouped by wavelength 12 waveband coverage of large areas of the sky Total about 200 TB data Doubling every 12 months Largest catalogues near 1B objects Data and images courtesy Alex Szalay, John Hopkins
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EGEE:Enabling Grids for E-Science in EuropeResourceCenter(Processors, disks)Grid server NodesOperationsCenterRegional SupportCenter(Support for ApplicationsLocal Resources)
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OverviewEvolution of scientific infrastructureTechnology drivers & new requirementsBuilding cyberinfrastructureStandards, software, and facilitiesExamplesFuturesVirtual dataDiscipline-specific cyberinfrastructuresChallenges
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Science as Workflow: Integrating & Evolving Data and Computation Science advances through tentative answers to a series of more & more subtle questions which reach deeper & deeper into the essence of natural phenomena. (L. Pasteur)In a networked petascale world, this means leveraging distributed data & computation toDiscover relevant data/hypothesesDerive new tentative data/hypotheses from oldPublish new data/hypotheses for use by othersIn a collaborative framework in which many can contribute to, & draw from, the whole
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Integrating AcrossDifferent Types of InformationID MURA_BACSU STANDARD; PRT; 429 AA.DE PROBABLE UDP-N-ACETYLGLUCOSAMINE 1-CARBOXYVINYLTRANSFERASEDE (EC 2.5.1.7) (ENOYLPYRUVATE TRANSFERASE) (UDP-N-ACETYLGLUCOSAMINEDE ENOLPYRUVYL TRANSFERASE) (EPT).GN MURA OR MURZ.OS BACILLUS SUBTILIS.OC BACTERIA; FIRMICUTES; BACILLUS/CLOSTRIDIUM GROUP; BACILLACEAE;OC BACILLUS.KW PEPTIDOGLYCAN SYNTHESIS; CELL WALL; TRANSFERASE.FT ACT_SITE 116 116 BINDS PEP (BY SIMILARITY).FT CONFLICT 374 374 S -> A (IN REF. 3).SQ SEQUENCE 429 AA; 46016 MW; 02018C5C CRC32; MEKLNIAGGD SLNGTVHISG AKNSAVALIP ATILANSEVT IEGLPEISDI ETLRDLLKEI GGNVHFENGE MVVDPTSMIS MPLPNGKVKK LRASYYLMGA MLGRFKQAVI GLPGGCHLGP RPIDQHIKGF EALGAEVTNE QGAIYLRAER LRGARIYLDV VSVGATINIM LAAVLAEGKT IIENAAKEPE IIDVATLLTS MGAKIKGAGT NVIRIDGVKE LHGCKHTIIP DRIEAGTFMI[source: GlaxoSmithKline via Carole Goble]
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Virtual Data Research:Sharing Recipes As Well as Data
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Discipline Cyberinfrastructure: E.g., Life SciencesLeverage emerging eScience infrastructureStorage, data archives, networks, clusterCreate an extensible service-rich environmentWorkflows (standard protocols)Experimental facilities: MRI, EM, arrays, Data update and access servicesComputational services: BLAST, etc., etc.Tools (informatics) suitesOntologies & conceptual discovery frameworksBio-applns-neutral: genomics, proteomics,
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Other ObservationsBuilding & applying cyberinstructure is hardNeed sustained effort and commitmentNot clear that current funding models applyMust support a critical mass of experts to build & operate infrastructureSoftware is a vital part of the wholeDiscipline sciences must obtain experienceStart multidisciplinary exploratory projectsInternational cooperation is vitalOur partners get it and are hard at work
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For More InformationThe Globus Alliancewww.globus.orgGlobal Grid Forumwww.ggf.orgGriPhyNwww.griphyn.orgBackground informationwww.mcs.anl.gov/~fosterGlobusWorld, Feb 2005www.globusworld.org2nd Edition: November 2003
Session Title
SJT: Updated titleNotare la complementarieta dei paesi di appartenenza dei partner di progetto Bioinformatics analyses typically involve visiting many data resources and analytical tools