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KAREN

VIEWS: 31 PAGES: 43

									KAREN


  What you can do with an
  advanced research and
  education network!
Introductions
 John and Sam
   We do not know your science
   We want to facilitate discussion
   This is an opportunity to report back to
    REANNZ on issues and barriers
 Who are you?



                                               2
Today’s Plan
   Introduction
   Collaboration – now and in the future
   Lunch
   Tools
   Capability Development
   Wrap up



                                            3
Introduction
 Motivation
   A paradigm shift
 Research Networks
 E-Research
   What is it?
 International trends
   Examples


                         4
The New Research Paradigm




Credit: GEANT2              5
Case Study:
Serious Disease Genes Revealed
 Wellcome Trust Case Control Consortium
 50 research groups
 200 scientists
 DNA from 17,000 patients
 15,000 polymorphic
  markers
 Learned more in 12
  months than last 15 years


                                           6
Case Study:
Functional MRI (fMRI) Data Center
 Online repository of
  neuroimaging data
 A typical study comprises
   3 groups
   20 subjects/group
   5 runs/subject
   300 volumes/run
    90,000 volumes, 60
      GB raw data
    1.2 million files
      processed
 100s of such studies in total

Credit Ian Foster, University of Chicago   7
www.fmridc.org

                 8
 Global R&E Network Pathways




DISCLAIMER - This network map was a best estimate of expected connectivity for 2005, several
changes in connectivity and planned connectivity have happened since it was created
Credit: John Silvester, USC, Chair CENIC                                                       9
   Kiwi Advanced Research and
   Education Network




                                         10
Credit: KAREN. http://www.karen.net.nz
 KAREN
 Went live Dec 2006
 10Gb/s NZ Backbone
 $40million, Government Funding
 $5million Capability Build Programme
 Linking all 8 Universities and all 9 Crown
  Research Institutes, + National Library
 ~622Mb/s link to US
 ~133Mb/s link to Australia

Credit: KAREN. http://www.karen.net.nz         11
Advanced Research and Education
Networks (ARENs)           Credit: GEANT2




                                     12
What is e-Research?
 Collaboration
 Access to and management of data and
  knowledge
 Advanced computing methods
 Shared resources
 New research techniques



                                         13
 Characterising e-Research
 Characteristic             Traditional Research                      E-Research
 Participants               Individual researcher or small            Diversely skilled, distributed
                            local research team                       research team

 Data                       Locally generated, stored and             Generated, stored and
                            accessible                                accessible from distributed
                                                                      locations
 Computation and            Batch compute jobs or jobs run Large-scale, or on demand
 Instrumentation            on researcher’s own computers computation or access to
                            or research instruments        shared instruments
 Networking                 Not reliant on networks                   Reliant on research networks
                                                                      and middleware

 Dissemination of           Via print publications or                 Via web sites and specialized
 Research                   conference presentations                  web portals


Credit: Bill Appelbe and David Bannon, Victorian Partnership for Advanced Computing. eResearch: Paradigm   14
Shift or Propaganda? http://www.jrpit.acs.org.au/jrpit/JRPITVolumes/JRPIT39/JRPIT39.2.83.pdf
Discussion
 Where does your research fit into this
  characterisation of traditional research and
  e-research?
 How does this compare with the research
  that you were doing 5 years ago?




                                             15
Current Environment - Set of Tools

                                            experiment
                             video
                                                         instrument
                           conference



                       web                                       data
                                            scientist
                       sites                                   storage



                               email                       HPC
                                            analysis



Credit: BeSTGrid. http://www.bestgrid.org                                16
Future Environment
Research Collaboratories
                                                web                 instrument
    scientist                                  portals
                         Grid Middleware
                                                                                 experiment
    scientist
                                                              HPC
                                           messaging

    scientist                                                                       data
                                                                                  storage

                                                   video
    scientist
                                                 conference         analysis



Credit: BeSTGrid. http://www.bestgrid.org                                                17
 The Researcher’s View
  Why do I care?
        New collaborative opportunities
        New funding opportunities
        NZ competitiveness
  What’s in it for me?
        Key resource is often somewhere else
        More data, more tools
        Collaborating with the best
  How do I get involved?
        Move from silo to GRID

Credit: BeSTGrid. http://www.bestgrid.org       18
Example e-Research Projects
 BioCoRE
 SCOOP
 SEEK/EcoGrid




                              19
20
BioCoRE
   Seamlessly access local and remote technology
   Co-author papers
   Access high performance computing
   Share molecular visualisations
   Chat room
   Lab book
   Notifications, etc.
   http://www.ks.uiuc.edu/Research/biocore/

                                                    21
The Control Panel




                    22
Projects




           23
Project Summary
                   Review
                     State of recent
                      job submissions
                     Who is logged in
                     What tasks
                      members are
                      working on
                     Recent
                      discussion topics
                     Recent files
                      added to BioFS

                                     24
Project Status
                  See
                    Current work
                    Future work
                  Modify
                    Schedule of
                     upcoming
                     tasks
                  Display
                    Current task


                                    25
Publishing VMD Sessions




                          26
Configuring NAMD Simulations




                               27
Job Management
                  A Grid Portal
                    Submit web form
                    Monitor progress
                  BioCoRE
                      Obtains resources
                      Moves files
                      Executes jobs
                      Places results




                                     28
Message Board




                29
Lab Book




           30
Website Library




                  31
BioCoRE File System




                      32
SURA Coastal Ocean Observing
and Predicting Programme




                               33
SCOOP
 Promote effective and rapid fusion of
  observed oceanic data with numerical
  models
 Facilitate the rapid dissemination of
  information to operational, scientific, and
  public or private users
 http://scoop.sura.org/


                                                34
SCOOP Goals
 Create an open access, distributed
  lab for oceanography by:
   Supporting data standards development and
    implementation
   Demonstrating benefits/added value of
    diverse communities moving to common
    standards for info exchange
   Creating an environmental prediction
    system –a research tool that can also
    support relevant agency decision-making to
    improve society

                                             35
Real-Time Ensemble
Prediction




Each forecast wind field is used as input for numerical predictions of
For verification, all relevant and available observations are
Hurricane warnings of the
Results from each analysis the NOAA National ensembleCenter in
The resultsandthe issued byBecause each individual element in this
                                are visualized and disseminated
storm surge of wave fields.predictions in the Hurricane are
              and compared with predictions, wind fields.
aggregatedsurge create an ensemble of involves awhich provides a
ensemble of can be readily incorporated into decision-support
(NHC) are used to and wave predictions forecast numerical
a form that
then aggregated for analysis. Results include maps that
real-time measure of accuracy andon a large supercomputer
                                          quality for
Each of these wind fields represents a with street the
show the by could take inundation
tools usedthat emergency response personnel. level detail.
calculation probability of many hoursplausible set of forecast winds
predictions.are farmed out to the available computational resources
cluster, they
over the entire region of interest for several days into the future.
within the distributed network.
                                                                         36
Distributed Facility for
Coastal Prediction

                              wind
                            forecasts
                           water level
                           OpenIOOS
                              model
                           wave watch
                               data
                              model




                                  37
Science Environment for
Ecological Knowledge
 Aims to extend ecological and biodiversity
  research capabilities by fundamentally
  improving how researchers:
    gain global access to ecological data and
     information
    find and use distributed computational services
    exercise powerful new methods for capturing,
     reproducing & analysing data
 http://seek.ecoinformatics.org/


                                                       38
SEEK’s Integrated Systems
 EcoGrid
   Next generation internet architecture enables
    data storage, sharing, access and analysis
 Semantic Mediation System
   Advanced reasoning system determines if
    data and analytical components can be
    automatically used in a selected workflow
 Analysis and Modeling System
   Ecologists design, modify and incorporate
    analyses to compose new workflows and
    models in a visual, automated environment

                                                39
EcoGrid
 Seamless access to and manipulation of data
  and metadata stored at different nodes
 Authentication via single sign-on
 Web services for executing analytical pipelines
 Registry of data and compute nodes
 Rapid ingest of new data sources as well as
  decades of legacy data
 Extensible relevant metadata based on the
  Ecological Metadata Language
 Data replication provides fault tolerance,
  disaster recovery and load balancing

                                                40
Kepler Workflow Tool
 Example of the 'R' system in a Kepler workflow




                                               41
Things to take away
 The research lifecycle is changing – an
  evolution rather than a sea-change
 Bigger and more complex problems require
  new methodologies and relationships
 Policy and funding are increasingly
  dictating collaboration
 Advanced networks are essential
 It’s more about data than technology
 Many social and organisational factors
                                         42
A Final Message




Credit: GEANT2    43

								
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