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Natural Disaster Mitigation:
   The LA Grid Initiative
Héctor A. Durán-Limón, University of Guadalajara
Content


LA Grid Initiative

Disaster Mitigation Needs

The WRF Web Portal: A case example

Challenges ahead

Conclusions
LA Grid Initiative
LA Grid
LA Grid Initiative


Main Goals
  Education
  Research
  Collaboration
  Talent development

Strategy: High impact areas
  Hurricane mitigation
  Health care
LA Grid Initiative


The main outcome so far since 2006
  Talent development
  Collaboration network
  Technology development for disaster mitigation
Disaster Mitigation Needs
Disaster Mitigation Needs


We have faced devastating disasters in the last years
  Tsunamis
      Japan 2011
      Indonesia and Thailand 2004
   Earthquakes
      Haiti 2010
      China 2010
      Chile 2010
   Hurricanes
      Katrina August 2005
      Wilma October 2005
Disaster Mitigation Needs


Better prediction capacity is required to generate better
evacuation plans
   Finer grained predictions
      Specific geographical area
   Predictions should be obtained in time
   Federated support vs Centralised support
Disaster Mitigation Needs


Prediction models usually require a large amount of
computing power

Grid and Cloud Computing can potentially bring more
power than Clusters
The WRF Web Portal: A case
        example
The WRF Web Portal


The Weather Research and Forecasting (WRF)
  Gaining acceptance as one of the best models
  Can also be used to predict evolution of forest fires and predict
  air pollution
  However, demands a huge amount of CPU power
The WRF Web Portal


WRF Web Portal
 On demand runs of WRF
     Available for meteorologist of all Mexican States
     Support for:
        More accurate weather prediction on a local area
        Research on meteorology
  Private cloud computing environment
  Challenge
     Increase performance while retain benefits of cloud computing
     environment
Challenges
Cloud Computing and HPC


Examples of HPC applications include:
  Weather Forecasting
  Quantum Chemistry Calculations
  Accelerator Modelling
  Astrophysical Simulations

This kind of Apps have huge demands of computing
resources
Traditionally supported by cluster- or Grid-based systems
Cloud Computing and HPC


Has experienced a tremendous growth recently

Offers a number of advantages over other distributed paradigms.
Access to resources is
   Flexible
   cost-effective. No need to pay large amount of money for
      Infrastructure
      Salaries for maintenance functions

The possibility of using Cloud computing for HPC Apps is
attractive
Cloud Computing and HPC


Current cloud computing platforms are not suitable for
running some of these applications

The performances offered is very poor due to the extensive
use of virtualisation

The performance is specially degraded when there is a huge
amount of IO operations

No guarantees for QoS (i.e. execution time) can be offered
Performance Prediction


Allows for Capacity planning

Helps to establish and maintain a Service Level Agreement
(SLA)

Most approaches either focus on
  a specific application
  a specific platform configuration
Performance Prediction


Need for supporting the following characteristics
  Application agnostic
  Platform configuration independency
  Multicore systems
  Virtualised environments
Conclusions


We presented the LA Grid initiative

We have identified some needs in natural disaster
mitigation

The WRF Web Portal case example was presented

Some challenges were outlined
Thank you!


        Hector Duran-Limon
  University of Guadalajara, Mexico

        hduran@cucea.udg.mx
http://maestro.cucea.udg.mx/~hduran/

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Hector duran limon

  • 1. Natural Disaster Mitigation: The LA Grid Initiative Héctor A. Durán-Limón, University of Guadalajara
  • 2. Content LA Grid Initiative Disaster Mitigation Needs The WRF Web Portal: A case example Challenges ahead Conclusions
  • 5. LA Grid Initiative Main Goals Education Research Collaboration Talent development Strategy: High impact areas Hurricane mitigation Health care
  • 6. LA Grid Initiative The main outcome so far since 2006 Talent development Collaboration network Technology development for disaster mitigation
  • 8. Disaster Mitigation Needs We have faced devastating disasters in the last years Tsunamis Japan 2011 Indonesia and Thailand 2004 Earthquakes Haiti 2010 China 2010 Chile 2010 Hurricanes Katrina August 2005 Wilma October 2005
  • 9. Disaster Mitigation Needs Better prediction capacity is required to generate better evacuation plans Finer grained predictions Specific geographical area Predictions should be obtained in time Federated support vs Centralised support
  • 10. Disaster Mitigation Needs Prediction models usually require a large amount of computing power Grid and Cloud Computing can potentially bring more power than Clusters
  • 11. The WRF Web Portal: A case example
  • 12. The WRF Web Portal The Weather Research and Forecasting (WRF) Gaining acceptance as one of the best models Can also be used to predict evolution of forest fires and predict air pollution However, demands a huge amount of CPU power
  • 13. The WRF Web Portal WRF Web Portal On demand runs of WRF Available for meteorologist of all Mexican States Support for: More accurate weather prediction on a local area Research on meteorology Private cloud computing environment Challenge Increase performance while retain benefits of cloud computing environment
  • 15. Cloud Computing and HPC Examples of HPC applications include: Weather Forecasting Quantum Chemistry Calculations Accelerator Modelling Astrophysical Simulations This kind of Apps have huge demands of computing resources Traditionally supported by cluster- or Grid-based systems
  • 16. Cloud Computing and HPC Has experienced a tremendous growth recently Offers a number of advantages over other distributed paradigms. Access to resources is Flexible cost-effective. No need to pay large amount of money for Infrastructure Salaries for maintenance functions The possibility of using Cloud computing for HPC Apps is attractive
  • 17. Cloud Computing and HPC Current cloud computing platforms are not suitable for running some of these applications The performances offered is very poor due to the extensive use of virtualisation The performance is specially degraded when there is a huge amount of IO operations No guarantees for QoS (i.e. execution time) can be offered
  • 18. Performance Prediction Allows for Capacity planning Helps to establish and maintain a Service Level Agreement (SLA) Most approaches either focus on a specific application a specific platform configuration
  • 19. Performance Prediction Need for supporting the following characteristics Application agnostic Platform configuration independency Multicore systems Virtualised environments
  • 20. Conclusions We presented the LA Grid initiative We have identified some needs in natural disaster mitigation The WRF Web Portal case example was presented Some challenges were outlined
  • 21. Thank you! Hector Duran-Limon University of Guadalajara, Mexico hduran@cucea.udg.mx http://maestro.cucea.udg.mx/~hduran/