power point (9.7mby) - advanced computing for science
TRANSCRIPT
Computing and Data Gridsfor Science and Engineering
www.ipg.nasa.govdoesciencegrid.org
William E. Johnstonhttp://www-itg.lbl.gov/~wej/
Computational Research Division,DOE Lawrence Berkeley National Laboratory
and
NASA Advanced Supercomputing (NAS) Division,
NASA Ames Research Center
6/24/03
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The Process of Large-Scale Science is Changing
• Large-scale science and engineering problems require collaborative use of many compute, data, and instrument resources all of which must be integrated with application components and data sets that are– developed by independent teams of researchers– or are obtained from multiple instruments– at different geographic locations
The evolution to this circumstance is what has driven my interest in high-speed distributed computing – now Grids – for 15 years.
E.g., see [1], and below.
Complex Infrastructure is Needed forSupernova Cosmology
Carbon Assimilation
CO2 CH4
N2O VOCsDust
HeatMoistureMomentum
ClimateTemperature, Precipitation,Radiation, Humidity, Wind
ChemistryCO2, CH4, N2O
ozone, aerosols
MicroclimateCanopy Physiology
Species CompositionEcosystem StructureNutrient Availability
Water
DisturbanceFiresHurricanesIce StormsWindthrows
EvaporationTranspirationSnow MeltInfiltrationRunoff
Gross Primary ProductionPlant RespirationMicrobial RespirationNutrient Availability
Ecosystems
Species CompositionEcosystem Structure
WatershedsSurface Water
Subsurface WaterGeomorphology
Biogeophysics
En
erg
y
Wa
ter
Ae
ro-
dyna
mic
s
Biogeochemistry
MineralizationDecomposition
Hydrology
Soi
l W
ater
Sno
w
Inte
r-ce
pted
Wat
er
Phenology
Bud Break
Leaf Senescence
HydrologicCycle
VegetationDynamics
Min
ute
s-T
o-H
ou
rsD
ays-
To
-We
eks
Yea
rs-T
o-C
entu
rie
sThe Complexity of a “Complete” Approach to Climate Modeling –
Terrestrial Biogeoscience Involves Many Interacting Processes and Data
(Courtesy Gordon Bonan, NCAR: Ecological Climatology: Concepts and Applications. Cambridge University Press, Cambridge, 2002.)
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Cyberinfrastructure for Science
• Such complex and data intensive scenarios require sophisticated, integrated, and high performance infrastructure to provide the– resource sharing and distributed data management,– collaboration, and– application frameworks
that are needed to successfully manage and carry out the many operations needed to accomplish the science
• This infrastructure involves– high-speed networks and services– very high-speed computers and large-scale storagehighly capable middleware, including support for
distributed data management and collaboration
6
The Potential Impact of Grids
• A set of high-impact science applications in the areas of– high energy physics– climate– chemical sciences– magnetic fusion energy
have been analyzed [2] to characterize their visions for the future process of science – how must science be done in the future in order to make significant progress
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The Potential Impact of Grids
• These case studies indicate that there is a great deal of commonality in the infrastructure that is required in every case to support those visions – including a common set of Grid middleware
• Further, Grids are maturing to the point where it is providing useful infrastructure for solving the computing, collaboration, and data problems of science application communities (e.g. as illustrated by the case studies, below)
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Grids: Highly Capable Middleware
• Core Grid services / Open Grid Services Infrastructure– Provide the consistent, secure, and uniform
foundation for managing dynamic and administratively heterogeneous pools of compute, data, and instrument resources
• Higher level services / Open Grid Services Architecture– Provide value-added, complex, and aggregated
services to users and application frameworks• E.g. information management –
Grid Data services that will provide a consistent and versatile view of data – real and virtual – of all descriptions
NERSCSupercomputing
& Large-Scale Storage
PNNL
LBNLANL
ESnet
Europe
DOEScience Grid ORNL
ESNet
X.509CA
Grid Managed ResourcesAsia-Pacific
Funded by the U.S. Dept. of Energy, Office of Science,Office of Advanced Scientific Computing Research,
Mathematical, Information, and Computational Sciences Division
Sys
tem
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anag
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t an
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Com
mun
icat
ion
Se
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es
Aut
hen
ticat
ion
Aut
horiz
atio
n
Sec
urity
S
ervi
ces
Grid
In
form
atio
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Ser
vice
Uni
form
Com
putin
gA
cces
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Uni
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Glo
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vent
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Au
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Mon
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Co-
Sch
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Uni
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Supernova Observatory
scientific instruments
Synchrotron Light Source
User Interfaces
Higher-level Services / OGSA (Data Grid Services, Workflow management, Visualization, Data Publication/Subscription, Brokering, Job Mg’mt, Fault Mg’mt, Grid System Admin., etc.)
Core Grid Services / OGSI: Uniform access to distributed resources
Applications (Simulations, Data Analysis, etc.)
Application Frameworks (e.g. XCAT, SciRun) and Portal Toolkits (e.g. XPortlets)
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Grids: Highly Capable MiddlewareAlso ….• Knowledge management
– Services for unifying, classifying and “reasoning about” services, data, and information in the context of a human centric problem solving environment – the Semantic Grid
– Critical for building problem solving environments that• let users ask “what if” questions• ease the construction of multidisciplinary systems
by providing capabilities so that the user does not have to be an expert in all of the disciplines to build a multidisciplinary system
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• Grids are also– A worldwide collection of researchers and
developers– Several hundred people from the US, European,
and SE Asian countries working on best practice and standards at the Global Grid Forum (www.gridforum.org)
– A major industry effort to combine Grid Services and Web Services (IBM, HP, Microsoft) (E.g. see [3])
– Vendor support from dozens of IT companies
Grid Middleware
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Web Services and Grids
• Web services provide for– Describing services (programs) with sufficient
information that they can be discovered and combined to make new applications (reusable components)
– Assembling groups of discovered services into useful problem solving systems
– Easy integration with scientific databases that use XML based metadata
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Web Services and Grids
• So …– Web Services provide for defining, accessing, and
managing serviceswhile– Grids provide for accessing and managing
dynamically constructed, distributed compute and data systems, and provide support for collaborations / Virtual Organizations
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Combining Web Services and Grids
• Combining Grid and Web services will provide a dynamic and powerful computing and data system that is rich in descriptions, services, data, and computing capabilities
• This infrastructure will give us the basic tools to deal with complex, multi-disciplinary, data rich science models by providing– for defining the interfaces and data in a
standard way – the infrastructure to interconnect those
interfaces in a distributed computing environment
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Combining Web Services and Grids
• This ability to utilize distributed services is important in science because highly specialized code and data is maintained by specialized research groups in their own environments, and it is neither practical nor desirable to bring all of these together on a single system
• The Terrestrial Biogeoscience climate system is an example where all of the components will probably never run on the same system – there will be manysub-models and associated data that are built and maintained in specialized environments
Carbon Assimilation
CO2 CH4
N2O VOCsDust
HeatMoistureMomentum
ClimateTemperature, Precipitation,Radiation, Humidity, Wind
ChemistryCO2, CH4, N2O
ozone, aerosols
MicroclimateCanopy Physiology
Species CompositionEcosystem StructureNutrient Availability
Water
DisturbanceFiresHurricanesIce StormsWindthrows
EvaporationTranspirationSnow MeltInfiltrationRunoff
Gross Primary ProductionPlant RespirationMicrobial RespirationNutrient Availability
Ecosystems
Species CompositionEcosystem Structure
WatershedsSurface Water
Subsurface WaterGeomorphology
Biogeophysics
En
erg
y
Wa
ter
Ae
ro-
dyna
mic
s
Biogeochemistry
MineralizationDecomposition
Hydrology
Soi
l W
ater
Sno
w
Inte
r-ce
pted
Wat
er
Phenology
Bud Break
Leaf Senescence
HydrologicCycle
VegetationDynamics
Min
ute
s-T
o-H
ou
rsD
ays-
To
-We
eks
Yea
rs-T
o-C
entu
rie
sTerrestrial Biogeoscience – A “Complete” Approach to Climate Modeling –
Involves Many Complex, Interacting Processes and Data
(Courtesy Gordon Bonan, NCAR: Ecological Climatology: Concepts and Applications. Cambridge University Press, Cambridge, 2002.)
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Combining Web Services and Grids
• The complexity of the modeling done in Terrestrial Biogeoscience is a touchstone for this stage of evolution of Grids and Web Services – this is one of the problems to solve in order to provide a significant increase in capabilities for science
• Integrating Grids and Web Services is a major thrust at GGF – e.g. in the OGSI and Open Grid Services Architecture Working Groups.Also see http://www.globus.org/ogsa/
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The State of Grids
• Persistent infrastructure is being built - this is happening, e.g., in– DOE Science Grid– NASA’s IPG– International Earth Observing Satellite Committee
(CEOS)– EU Data Grid– UK eScience Grid– NSF TeraGrid– NEESGrid (National Earthquake Engineering
Simulation Grid)
all of which are focused on large-scale science and engineering
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The State of Grids – Some Case Studies
• Further, Grids are becoming a critical element of many projects – e.g.– The High Energy Physics problem of
managing and analyzing petabytes of data per year has driven the development of Grid Data Services
– The National Earthquake Engineering Simulation Grid has developed a highly application oriented approach to using Grids
– The Astronomy data federation problem has promoted work in Web Services based interfaces
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High Energy Physics Data Management
• Petabytes of data per year must be distributed to hundreds of sites around the world for analysis
• This involves– Reliable, wide-area, high-volume data
management– Global naming, replication, and caching of
datasets– Easily accessible pools of computing resources
• Grids have been adopted as the infrastructure for this HEP data problem
Tier 1
Tier2 Center
Online System
eventreconstruction
French Regional Center
German Regional Center
Institute
Institute
Institute
Institute ~0.25TIPS
Workstations
~100 MBytes/sec
~0.6-2.5 Gbps
100 - 1000 Mbits/sec
Physics data cache
~PByte/sec
Tier2 CenterTier2 CenterTier2 Center
~0.6-2.5 Gbps
Tier 0 +1
Tier 3
Tier 4
Tier2 Center Tier 2
CERN/CMS data goes to 6-8 Tier 1 regional centers, and from each of these to 6-10 Tier 2 centers.
Physicists work on analysis “channels” at 135 institutes. Each institute has ~10 physicists working on one or more channels.
2000 physicists in 31 countries are involved in this 20-year experiment in which DOE is a major player.
CERN LHC CMS detector
15m X 15m X 22m, 12,500 tons, $700M.
human=2m
analysis
Italian Center FermiLab, USA Regional Center
Courtesy Harvey
Newman, CalTech
High Energy Physics Data Management CERN / LHC Data: One of Science’s most challenging data
management problems
~2.5 Gbits/sec
event simulation
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High Energy Physics Data Management
• Virtual data catalogues and on-demand data generation have turned out to be an essential aspect– Some types of analysis are pre-defined and
catalogued prior to generation - and then the data products are generated on demand when the virtual data catalogue is accessed
– Sometimes regenerating derived data is faster and easier than trying to store and/or retrieve that data from remote repositories
– For similar reasons this is also of great interest to the EOS (Earth Observing Satellite) community
US-CMS/LHC Grid Data Services Testbed:International Virtual Data Grid Laboratory
Virtual Data Tools
Data Generation Request
Planning &Scheduling Tools
Data GenerationRequest
Execution & Management Tools
Transforms
Distributed resources(code, storage, CPUs,networks)
Interactive User Tools
Raw datasource
•Metadata catalogues•Virtual data catalogues
ResourceManagement
Security andPolicy
Other GridServices Core Grid Services
metadatadescriptionof analyzed
data
CMS Event Simulation Productionusing GriPhyN Data Grid Services
• Production Run on the Integration Testbed (400 CPUs at 5 sites)– Simulate 1.5 million full CMS events for physics studies– 2 months continuous running across 5 testbed sites– Managed by a single person at the US-CMS Tier 1site Nearly 30 CPU years delivered 1.5 Million Events to CMS
Physicists
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Partnerships with the Japanese Science Community
• Comments of Paul Avery [email protected], director iVDGL– iVDGL is specifically interested in partnering with the Japanese
HEP community and hopefully the National Research Grid Initiative will opens doors for collaboration
– Science drivers are critical – existing international HEP collaborations in Japan provide natural drivers
– Different Japanese groups could participate in existing or developing Grid applications oriented testbeds, such as the ones developed in iVDGL for the different HEP experiments
• These testbeds have been very important for debugging Grid software while serving as training grounds for existing participants and new groups, both at universities and national labs.
– Participation in and development of ultra-speed networking projects provides collaborative opportunities in a crucial related area. There are a number of new initiatives that are relevant
• Contact Harvey B Newman <[email protected]> for a fuller description and resource materials.
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National Earthquake Engineering Simulation Grid
• NEESgrid will link earthquake researchers across the U.S. with leading-edge computing resources and research equipment, allowing collaborative teams (including remote participants) to plan, perform, and publish their experiments
• Through the NEESgrid, researchers will– perform tele-observation and tele-operation of
experiments – shake tables, reaction walls, etc.; – publish to, and make use of, a curated data repository
using standardized markup; – access computational resources and open-source
analytical tools; – access collaborative tools for experiment planning,
execution, analysis, and publication
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NEES Sites
• Shake Table Research Equipment– University at Buffalo, State
University of New York – University of Nevada, Reno – *University of California, San
Diego
• Centrifuge Research Equipment– *University of California, Davis – Rensselaer Polytechnic
Institute
• Tsunami Wave Basin– *Oregon State University,
Corvallis, Oregon
• Large-Scale Lifeline Testing– Cornell University
• Large-Scale Laboratory Experimentation Systems– University at Buffalo, State
University of New York – *University of California at
Berkeley– *University of Colorado,
Boulder – University of Minnesota-Twin
Cities – Lehigh University – University of Illinois, Urbana-
Champaign
• Field Experimentation and Monitoring Installations– *University of California, Los
Angeles– *University of Texas at Austin – Brigham Young University
Field Equipment
Laboratory Equipment
Remote Users
Remote Users: (K-12 Faculty and Students)
High-Performance Network(s)
Instrumented Structures and Sites
Large-scale Computation
Curated Data Repository
Laboratory Equipment
Global Connections
Simulation Tools Repository
NEESgrid Earthquake Engineering Collaboratory
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NEESgrid Approach
• Package a set of application level services and the supporting Grid software in a single“point of presence” (POP)
• Deploy the POP to a select set of earthquake engineering sites to provide the applications, data archiving, and Grid services
Assist in developing common metadata so that the various instruments and simulations can work together
• Provide the required computing and data storage infrastructure
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NEESgrid Multi-Site Online Simulation (MOST)
• A partnership between the NEESgrid team, UIUC and Colorado Equipment Sites to showcase NEESgrid capabilities
• A large-scale experiment conducted in multiple geographical locations which combines physical experiments with numerical simulation in an interchangeable manner
• The first integration of NEESgrid services with application software developed by Earthquake Engineers (UIUC, Colorado and USC) to support a real EE experiment
• See http://www.neesgrid.org/most/
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NEESgrid Multi-Site Online Simulation (MOST)
U. Colorado U. Colorado Experimental Experimental
SetupSetup
UIUC Experimental UIUC Experimental SetupSetup
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Multi-Site, On-Line Simulation Test (MOST)
ColoradoColorado
Experimental Model
gx
f2m1, θ1
F2
F1
e
gx=
gx
f1, x1
UIUCUIUC
Experimental Model
gx
m1
f1 f2
NCSANCSA
Computational Model
SIMULATIONSIMULATION
COORDINATORCOORDINATOR
NEESpop NEESpop
NEESpop
UIUC MOST-SIM•Dan Abrams•Amr Elnashai•Dan Kuchma•Bill Spencer• and othersColorado FHT•Benson Shing•and others
1994 Northridge Earthquake SimulationRequires a Complex Mix of Data and Models
Pier #7
Pier #8
Pier #5
Pier #6
Amr Elnashai, UIUC
NEESgrid provides the common data formats, uniform dataarchive interfaces, and computational services needed to supportthis multidisciplinary simulation
Laboratory EquipmentInstrumented
Structures and Sites
Large-scale Computation
Curated Data Repository Simulation
Tools Repository
NEESgrid Architecture
Data AcquisitionSystem
Large-scale Storage
Video Services
E-NotebookServices
GridFTP
MetadataServices
CompreHensive collaborativEFramework (CHEF)
NEESGrid StreamingData System
NEESpop
Java AppletWeb
BrowserUser Interfaces
NEES distributed resources
Grid Services
SIMULATIONSIMULATION
COORDINATORCOORDINATOR
Accounts &MyProxy
NEESgridMonitoring
NEES Operations
ExperimentsMultidisciplinary
SimulationsCollaborations
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Partnerships with the Japanese Science Community
• Comments of Daniel Abrams <[email protected]>, Professor of Civil Engineering, University of Illinois and NEESGrid project manager– The Japanese earthquake research community has expressed interest
in NEESgrid– I am aware of some developmental efforts between one professor and
another to explore feasibility of on-line pseudodynamic testing - Professor M. Watanabe at the University of Kyoto is running a test in his lab which is linked with another test running at KAIST (in Korea) with Professor Choi. They are relying on the internet for transmission of signals between their labs.
– International collaboration with the new shaking table at Miki is being encouraged and thus they are interested in plugging in to an international network. There is interest in NEESgrid in installing a NEESpop there so that the utility could be evaluated, and connections made with the NEESGrid sites.
– We already have some connection to the Japanese earthquake center known as the Earthquake Disaster Mitigation Center. We have an MOU with EDM and the Mid-America Earthquake Center in place. I am working with their director, Hiro Kameda, and looking into establishing a NEESGrid relationship.
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The Changing Face of Observational Astronomy
• Large digital sky surveys are becoming the dominant source of data in astronomy: > 100 TB, growing rapidly– Current examples: SDSS, 2MASS, DPOSS, GSC,
FIRST, NVSS, RASS, IRAS; CMBR experiments; Microlensing experiments; NEAT, LONEOS, and other searches for Solar system objects …
– Digital libraries: ADS, astro-ph, NED, CDS, NSSDC– Observatory archives: HST, CXO, space and ground-
based– Future: QUEST2, LSST, and other synoptic surveys;
GALEX, SIRTF, astrometric missions, GW detectors
• Data sets orders of magnitude larger, more complex, and more homogeneous than in the past
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The Changing Face of Observational Astronomy
• Virtual Observatory: Federation of N archives– Possibilities for new discoveries grow as O(N2)
• Current sky surveys have proven this– Very early discoveries from Sloan (SDSS),
2 micron (2MASS), Digital Palomar (DPOSS)
• see http://www.us-vo.org
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Sky Survey Federation
Mining Data from Dozens of Instruments / Surveys is Frequently a Critical Aspect of Doing Science
• The ability to federate survey data is enormously important
• Studying the Cosmic Microwave Background – a key tool in studying the cosmology of the universe – requires combined observations from many instruments in order to isolate the extremely weak signals of the CMB
• The datasets that represent the material “between” us and the CMB are collected from different instruments and are stored and curated at many different institutions
• This is immensely difficult without approaches like National Virtual Observatory in order to provide a uniform interface for all of the different data formats and locations
(Julian Borrill, NERSC, LBNL)
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NVO Approach
• Focus is on adapting emerging information technologies to meet the astronomy research challenges– Metadata, standards, protocols (XML, http)– Interoperability– Database federation– Web Services (SOAP, WSDL, UDDI)– Grid-based computing (OGSA)
• Federating data bases is difficult, but very valuable– An XML-based mark-up for astronomical tables and catalogs -
VOTable– Developed metadata management framework– Formed international “registry”, “dm” (data models), “semantics”,
and “dal” (data access layer) discussion groups
• As with NEESgrid, Grids are helping to unify the community
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NVO Image Mosaicking
• Specify box by position and size• SIAP server returns relevant images
• Footprint• Logical Name• URL
Can choose:
standard URL:http://.......
SRB URLsrb://nvo.npaci.edu/…..
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Atlasmaker Virtual Data System
Metadata repositoriesFederated by OAI
Data repositoriesFederated by SRB
Compute resourcesFederated by TG/IPG
Mosaicked data is on
file
2a. Mosaicked data is not on file
2d: Store result &
return result
2c: Compute on TG/IPG
Userrequest
Request manager
2b. Get raw data from NVO resources
Core Grid Services
Higher LevelGrid Services
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Background Correction
Uncorrected Corrected
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NVO Components
Simple Image Access ServicesCone Search Services
UCDs
Visualization
Web Services
Grid Services
Cross-Correlation Engine
Resource/Service Registries
Streaming
VOTable VOTable
UCDs
Data archives Computing resources
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International Virtual Observatory Collaborations
• Astrophysical Virtual Observatory (European Commission)
• AstroGrid, UK e-scienceprogram
• Canada
• VO India
• VO Japan(leading the work on VO query language)
• VO China
• German AVO
• Russian VO
• e-Astronomy Australia
• IVOA(International Virtual Observatory Alliance)
US contacts: Alex Szalay [email protected], Roy Williams [email protected],Bob Hanisch <[email protected]>
Where to in the Future?The potential of a Semantic Grid / Knowledge Grid:
Combining Semantic Web Services and Grid Services
• Even when we have well integrated Web+Grid services we still do not provide enough structured information and tools to let us ask “what if” questions, and then have the underlying system assemble the required components in a consistent way to answer such a question.
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Beyond Web Services and Grids
• A commercial example “what if” question:– What does my itinerary look like if I wish to go
SFO to Paris, CDG, and then to Bucharest.– In Bucharest I want a 3 or 4 star hotel that is
within 3 km of the Palace of the Parliament, and the hotel cost may not exceed the U. S. Dept. of State, Foreign Per Diem Rates.
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Beyond Web Services and Grids
• To answer such a question – a relatively easy task, but tedious, for a human – the system must “understand” the relationships between maps and locations, between per diem charts and published hotel rates, and it must be able to apply constraints (< 3 km, 3 or 4 star,cost < $ per diem rates, etc.)
• This is the realm of “Semantic Grids”
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Semantic Grids / Knowledge Grids
• Work is being adapted from the Artificial Intelligence community to provide [4]
– “Ontology languages” to extend metadata to represent relationships
– Language constructs to express rule based / constraint relationships among, and generalizations of, the extended terms
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Future Cyberinfrastructureimpacttechnology
Object-oriented structure: Class definitions can be derived from multiple superclasses, and property definitions can specify domain and range constraints.
Can now represent tree structured information (e.g. Taxonomies)
Resource Description Framework Schema (RDFS) [7]
An extensible, object-oriented type system that effectively represents and defines classes.
Can ask questions like “What are a particular property’s permitted values, which types of resources can it describe, and what is its relationship to other properties.”
Resource Description Framework (RDF) [7]
Expresses relationships among “resources” (URI(L)s) in the form of object-attribute-value (property). Values of can be other resources, thus we can describe arbitrary relationships between multiple resources.
RFD uses XML for its syntax.
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Future Cyberinfrastructure
Knowledge representation and manipulation that have well defined semantics and representation of constraints and rules for reasoning
OWL (DAML+OIL) +…… [9]
impacttechnology
OIL can state conditions for a class that are both sufficient and necessary. This makes it possible to perform automatic classification: Given a specific object, OIL can automatically decide to which classes the object belongs.
This is functionality that should make it possible to ask the sort of constraint and relationship based questions illustrated above.
Ontology Inference Layer (OIL) [8]
OIL inherits all of RDFS, and adds expressing class relationships using combinations of intersection (AND), union (OR), and compliment (NOT). Supports concrete data types (integers, strings, etc.)
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Semantic Grid Capabilities
• Based on these technologies, the emerging Semantic Grid [6] / Knowledge Grid [5] services will provide several important capabilities
1) The ability to answer “what if” questions by providing constraint languages that operate on ontologies that describe content and relationships of scientific data and operations, thus “automatically” structuring data and simulation / analysis components into Grid workflows whose composite actions produce the desired information
53
Semantic Grid Capabilities
2) Tools, content description, and structural relationships so that when trying to assemble multi-disciplinary simulations, an expert in one area can correctly organize the other components of the simulation without having to involve experts in all of the ancillary sub-models (components)
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Future Cyberinfrastructure
• Much work remains to make this vision a reality
• The Grid Forum has recently established a Semantic Grid Research Group [10] to investigate and report on the path forward for combining Grids and Semantic Web technology.
55
Thanks to Colleagues who Contributed Material to this Talk
• Dan Reed, Principal Investigator, NSF NEESgrid; Director, NCSA and the Alliance; Chief Architect, NSF ETF TeraGrid; Professor, University of Illinois - [email protected]
• Ian Foster, Argonne National Laboratory and University of Chicago, http://www.mcs.anl.gov/~foster
• Dr. Robert Hanisch, Space Telescope Science Institute, Baltimore, Maryland
• Roy Williams, Cal Tech; Dan Abrams, UIUC; Paul Avery, Univ. of Florida; Alex Szalay, Johns Hopkins U.; Tom Prudhomme, NCSA
Grid Services: secure and uniform access and management for distributed resources
Science Portals: collaboration and problem solvingWeb Services
Supercomputing andLarge-Scale Storage
High Speed Networks
Spallation Neutron Source
High Energy Physics
Advanced Photon Source
Macromolecular Crystallography
Advanced Engine Design
Computing and Storage
of Scientific Groups
Supernova Observatory
Advanced Chemistry
57
Notes
[1] “The Computing and Data Grid Approach: Infrastructure for Distributed Science Applications,” William E. Johnston. http://www.itg.lbl.gov/~johnston/Grids/homepage.html#CI2002
[2] “DOE Office of Science, High Performance Network Planning Workshop.”August 13-15, 2002: Reston, Virginia, USA.http://doecollaboratory.pnl.gov/meetings/hpnpw
[3] “Developing Grid Computing Applications” in IBM developerWorks : Web services : Web services articleshttp://www-106.ibm.com/developerworks/library/ws-grid2/?n-ws-1252
[4] See “The Semantic Web and its Languages,” an edited collection of articles in IEEE Intelligent Systems, Nov. Dec. 2000. D. Fensel, editor.
[5] For an introduction to the ideas of Knowledge Grids I am indebted to Mario Cannataro, Domenico Talia, and Paolo Trunfio (CNR, Italy). See www.isi.cs.cnr.it/kgrid/
[6] For an introduction to the ideas of Semantic Grids I am indebted to Dave DeRoure (U. Southampton), Carol Gobel (U. Manchester), and Geoff Fox (U. Indiana). See www.semanticgrid.org
[7] “The Resource Description Framework,” O. Lassila. ibid.[8] “FAQs on OIL: Ontology Inference Layer,” van Harmelen and Horrocks. ibid. and
“OIL: An Ontology Infrastructure for the Semantic Web.” Ibid.[9] “Semantic Web Services,” McIlraith, Son, Zeng. Ibid. and
“Agents and the Semantic Web,” Hendler. Ibid.[10] See http://www.semanticgrid.org/GGF This GGF Research Group is co-chaired by
David De Roure <[email protected]>, Carole Goble <[email protected]>, and Geoffrey Fox <[email protected]>