# Presentation: 872269

## Computing and Data Grids

**for Science and Engineering **

***www.ipg.nasa.gov***

***doesciencegrid.org***

***William E. Johnston***
***http://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***

## 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.

**Notes:**
The point here is that most modern, large-scale, science and engineering is multi-disciplinary, and must use geographically distributed components, and this is what has motivated a $50-75M/yr (approx) investment in Grid technology by the US, UK, European, Japanese, and Korean governments.

## Complex Infrastructure is Needed forSupernova Cosmology

## The Complexity of a  “Complete” Approach to Climate Modeling –Terrestrial Biogeoscience Involves Many Interacting Processes and Data

- CO2 CH4
- N2O VOCs
- Dust

- Heat
- Moisture
- Momentum

- _Climate_
- Temperature, Precipitation,
- Radiation, Humidity, Wind

- _Chemistry_
- CO2, CH4, N2O
- ozone, aerosols

- Microclimate
- Canopy Physiology

- Species Composition
- Ecosystem Structure
- Nutrient Availability
- Water

- _Disturbance_
- Fires
- Hurricanes
- Ice Storms
- Windthrows

- Evaporation
- Transpiration
- Snow Melt
- Infiltration
- Runoff

- Gross Primary
- Production
- Plant Respiration
- Microbial Respiration
- Nutrient Availability

- Ecosystems
- Species Composition
- Ecosystem Structure

- WatershedsSurface Water
- Subsurface Water
- Geomorphology

- Biogeophysics

- Energy

- Water

- Aero-
- dynamics

- Biogeochemistry

- Mineralization

- Decomposition

- Hydrology

- Soil
- Water

- Snow

- Inter-
- cepted
- Water

- Phenology

- Bud Break

- Leaf Senescence

- Hydrologic
- Cycle

- Vegetation
- Dynamics

**Minutes-To-Hours**

**Days-To-Weeks**

**Years-To-Centuries**

**The 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.)

## 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 storage
- _highly capable middleware, including support for distributed data management and collaboration_

## 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

## 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)

## 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

## DOEScience Grid

**NERSC****Supercomputing****& Large-Scale Storage**

**PNNL**

**LBNL**

**ANL**

**ESnet**

**Europe**

**DOE****Science Grid**

**ORNL**

**ESNet**

**X.509**
**CA**

_**Grid Managed Resources**_

**Asia-Pacific**

**Funded by the U.S. Dept. of Energy, Office of Science,****Office of Advanced Scientific Computing Research,****Mathematical, Information, and Computational Sciences Division**

- Systems management and access

- Communication Services

- Authentication Authorization

- Security Services

- Grid  Information  Service

- UniformComputingAccess

- Unix and OGSI hosting

- Global EventServices, Auditing, Monitoring

- Co-Scheduling

- Uniform Data Access

**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)

## Grids: Highly Capable Middleware

- Also ....
- 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

## 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

- 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](http://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**

## 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

## Web Services and Grids

- So ...
- Web Services provide for defining, accessing, and managing services
- while
- Grids provide for accessing and managing dynamically constructed, distributed compute and data systems, and provide support for collaborations / Virtual Organizations

## 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

## 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 _many__sub-models and associated data that are built and maintained in specialized environments_

## Terrestrial Biogeoscience – A “Complete” Approach to Climate Modeling –  Involves Many Complex, Interacting Processes and Data

- CO2 CH4
- N2O VOCs
- Dust

- Heat
- Moisture
- Momentum

- _Climate_
- Temperature, Precipitation,
- Radiation, Humidity, Wind

- _Chemistry_
- CO2, CH4, N2O
- ozone, aerosols

- Microclimate
- Canopy Physiology

- Species Composition
- Ecosystem Structure
- Nutrient Availability
- Water

- _Disturbance_
- Fires
- Hurricanes
- Ice Storms
- Windthrows

- Evaporation
- Transpiration
- Snow Melt
- Infiltration
- Runoff

- Gross Primary
- Production
- Plant Respiration
- Microbial Respiration
- Nutrient Availability

- Ecosystems
- Species Composition
- Ecosystem Structure

- WatershedsSurface Water
- Subsurface Water
- Geomorphology

- Biogeophysics

- Energy

- Water

- Aero-
- dynamics

- Biogeochemistry

- Mineralization

- Decomposition

- Hydrology

- Soil
- Water

- Snow

- Inter-
- cepted
- Water

- Phenology

- Bud Break

- Leaf Senescence

- Hydrologic
- Cycle

- Vegetation
- Dynamics

**Minutes-To-Hours**

**Days-To-Weeks**

**Years-To-Centuries**

**Terrestrial 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.)

## 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/](http://www.globus.org/ogsa/)_

## 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

## 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

## 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_

## High Energy Physics Data Management CERN / LHC Data: One of Science’s most challenging data management problems

**Online System**

**event****reconstruction**

**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**

**~0.6-2.5 Gbps**

***Tier 0 +1***

***Tier 3***

***Tier 4***

***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**

- HPSS

- 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

## 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 Generation****Request****Execution &**** Management Tools**

***Transforms***

**Distributed resources****(code, storage, CPUs,****networks)**

***Interactive User Tools***

**Raw data****source**

***Metadata catalogues***
***Virtual data catalogues***

**Resource****Management**

**Security and****Policy**

**Other Grid****Services**

**Core Grid Services**** **

- metadatadescriptionof analyzeddata

## CMS Event Simulation Productionusing GriPhyN Data Grid Services

**CMS Event Simulation Production****using 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**

## Partnerships with the Japanese Science Community

- Comments of Paul Avery _[avery@phys.ufl.edu](mailto:avery@phys.ufl.edu)__, 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 <_[newman@hep.caltech.edu](mailto:newman@hep.caltech.edu)__> for a fuller description and resource materials._

## 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

## 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

## NEESgrid Earthquake Engineering Collaboratory

**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**

## 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_

## 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/

## NEESgrid Multi-Site Online Simulation (MOST)

**NEESgrid Multi-Site Online Simulation (MOST)**

**U. Colorado Experimental Setup**

**UIUC Experimental Setup**

**Notes:**
Again the height of frame has to be determined, where the column to be tested at U. of Colorado plays a key role for determining the height, as this set-up is already build at U. of Colorado.

## Multi-Site, On-Line Simulation Test (MOST)

**Multi-Site, On-Line Simulation Test (MOST)**

**Colorado**
***Experimental Model***

**UIUC***** ***
***Experimental Model***

**NCSA**** **
***Computational Model***

- SIMULATION
- COORDINATOR

- UIUC MOST-SIM
- Dan Abrams
- Amr Elnashai
- Dan Kuchma
- Bill Spencer
-  and others
- Colorado FHT
- Benson Shing
- and others

## 1994 Northridge Earthquake SimulationRequires 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

## NEESgrid Architecture

**Laboratory Equipment**

**Instrumented Structures and Sites**

**Large-scale Computation **

**Curated Data Repository**

**Simulation Tools Repository**

**NEESgrid Architecture**

**Data Acquisition****System**

**Large-scale Storage **

**Video Services**

**E-Notebook**
**Services**

**GridFTP**

**Metadata**
**Services**

**CompreHensive collaborativE**
**Framework (CHEF)**

**NEESGrid Streaming****Data System**

**NEESpop**

**Java Applet**

**Web**
**Browser**

**User Interfaces**

_**NEES distributed resources**_

**Grid Services**

- SIMULATION
- COORDINATOR

**Accounts &**
**MyProxy**

**NEESgrid**
**Monitoring**

**NEES Operations**

**Experiments**

**Multidisciplinary****Simulations**

**Collaborations**

## Partnerships with the Japanese Science Community

- Comments of Daniel Abrams <d-abrams@uiuc.edu>, 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.

## 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

## 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

## 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)

## 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

## NVO Image Mosaicking

-  Specify box by position and size
-  SIAP server returns relevant images
-  *Footprint*
* **Logical Name*
* **URL*

- Can choose:
- standard URL:
- http://.......
- SRB URL
- srb://nvo.npaci.edu/.....

## Atlasmaker Virtual Data System

**Metadata repositories**
**Federated by OAI**

**Data repositories**
**Federated by SRB**

**Compute resources**
**Federated by TG/IPG**

**2d: Store result & return result**

**2c: Compute on TG/IPG**

- Core Grid Services

- Higher LevelGrid Services

## Background Correction

- Uncorrected

- Corrected

## NVO Components

- Simple Image Access Services

- Cone Search Services

- UCDs

- Visualization

- Web Services

- Grid Services

- Cross-Correlation Engine

- Resource/Service Registries

- Streaming

- VOTable

- VOTable

- UCDs

- Data archives

- Computing resources

## Astrophysical Virtual Observatory (European Commission)

AstroGrid, UK e-scienceprogram

Canada

VO India 

VO Japan
	(leading the work on VO query language)

VO China

- 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 _[szalay@jhu.edu](mailto:szalay@jhu.edu)__, Roy Williams __[roy@cacr.caltech.edu](mailto:roy@cacr.caltech.edu)__,__Bob Hanisch <hanisch@stsci.edu>_

## 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.

## 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.

## 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”

## 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

## Future Cyberinfrastructure

| technology | impact |
| --- | --- |
| 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. | 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 Schema (RDFS) [7]
An extensible, object-oriented type system that effectively represents and defines classes. | 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) |

## Future Cyberinfrastructure

| technology | impact |
| --- | --- |
| 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.) | 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. |
| OWL (DAML+OIL) +...... [9] | Knowledge representation and manipulation that have well defined semantics and representation of constraints and rules for reasoning |

## 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

## 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)

## 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.

## 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 - _[reed@ncsa.uiuc.edu](mailto:reed@ncsa.uiuc.edu)_
- Ian Foster, Argonne National Laboratory and University of Chicago,  _[http://www.mcs.anl.gov/~foster](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

## Integrated Cyberinfrastructure for Science

**Grid Services:**** secure and uniform access and management for distributed resources**

**Science Portals:**** ****collaboration and problem solving**

**Web Services**

**Supercomputing and****Large-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**

## 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](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](http://doecollaboratory.pnl.gov/meetings/hpnpw)_
- [3] “Developing Grid Computing Applications” in IBM developerWorks : Web services : Web services articles_[http://www-106.ibm.com/developerworks/library/ws-grid2/?n-ws-1252](http://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](http://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 <dder@ecs.soton.ac.uk>, Carole Goble <cgoble@cs.man.ac.uk>, and Geoffrey Fox <gcf@grids.ucs.indiana.edu>