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Introduction to FutureGrid: Towards a Computing Testbed as a Service

Introduction to FutureGrid: Towards a Computing Testbed as a Service. Gregor von Laszewski. U se my own cluster?. Pro: Full access Can support the research I am interested in Con: Limited scale Maintenance cost high Does often do not integrate with multitenancy research

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Introduction to FutureGrid: Towards a Computing Testbed as a Service

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  1. Introduction to FutureGrid:Towards a Computing Testbed as a Service Gregor von Laszewski

  2. Use my own cluster? • Pro: • Full access • Can support the research I am interested in • Con: • Limited scale • Maintenance cost high • Does often do not integrate with multitenancy research • Limitation of research topics due to availability of testbed capabilities (often decommissioned hardware)

  3. Use XSEDE-other than FG? • Pro: • Full managed environment • Software can be added by staff but must undergo production testing and application need analysis • Scale • Con: • Focused on application use not CS research • Software stack is “static” • Research limited to application optimization • Targets production software not testing • Resources are mostly dedicated to production • Experiments could impact production

  4. CS Research Testbeds (A personal view) • Grids: • Wisconsin Grid Testbed: Compile service, limited testing  • HPC: • XSEDE TIS: Focus on XSEDE resources, production level, limited innovation ability  • EmuLab: • Focus on network research, bare metal  • FutureGrid: • Integrates with bare metal, IaaS, PaaS, limited network research 

  5. What others want on FG

  6. What others want on FG OpenStack

  7. Google Trends

  8. Recent Trends • FG (Project Trends) • All IaaS same interest volume • OpenStack  • OpenNebula  • Nimbus  • Eucalyptus  • Eucalyptus (Class)  • Google (User Trends) • OpenStack  • CloudStack • Eucalyptus  • Nimbus 

  9. Why don’t we support …? • IU • Model: provisioning by users and center • IU supports • HPC (IU&SDSC) • OpenStack • Eucalyptus (IU&SDSC) • Hadoop • various other activities • UFL • Model: traditional • Cloudstack • TACC & UC • Model : traditional • HPC • Nimbus

  10. SW Architecture: Integrative View

  11. Image Generation • Users who want to create a new FG image specify the following: • OS type • OS version • Architecture • Kernel • Software Packages • Image is generated, then deployed to specified target. • Deployed image gets continuously scanned, verified, and updated. • Images are now available for use on the target deployed system. http://futuregrid.org

  12. Provisioning HPC, Grid, and Cloud Services

  13. Management Services • Image Management • Dynamic Provisioning • Experiment Management • Monitoring and Information Services If image is not available

  14. Management Services • Image Management • Dynamic Provisioning • Experiment Management • Monitoring and Information Services

  15. Experiment ManagementGoals • Support rigorous experimentation • Define experiments in detail • Record experimental results • User-specified measurements (placement and granularity) • Share experiment information • Experiments can be repeated and verified • Variations on experiments can be performed • Convenient execution of experiments • FutureGrid has distributed resources and services • Supports different user scenarios

  16. Management Services • Image Management • Dynamic Provisioning • Experiment Management • Monitoring and Information Services

  17. fg-rain –h hostfile –image img • fg-rain –h hostfile –iaasopenstack–image img • fg-rain –h hostfile –paashadoop… • Users require not a complex experiment environment, but a high-level interface to it • We need more than a “workflow” enectment engine • fg-shell > ….. (part of fg-rain) Using Rain as Experiment Management Tool http://futuregrid.org

  18. Assemble your own Experiment • Users have control of the entire stack • Testbed Production systems adapt based on user and service demand

  19. Summary

  20. Selected Testbed Capabilities

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