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Scalarm: Scalable Platform for Data Farming

1. Scalarm: Scalable Platform for Data Farming. D. Król , Ł. Dutka, M. Wrzeszcz, B. Kryza, R. Słota and J. Kitowski. ACC Cyfronet AGH. KU KDM, Zakopane, 2013. Agenda. 2. About Us Problem statement Solution Contact. Who are we ?. 3. Computer Systems Group AGH

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Scalarm: Scalable Platform for Data Farming

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  1. 1 Scalarm: Scalable Platform for Data Farming D. Król, Ł. Dutka, M. Wrzeszcz, B. Kryza, R. Słota and J. Kitowski ACC Cyfronet AGH KU KDM, Zakopane, 2013

  2. Agenda 2 About Us Problem statement Solution Contact

  3. Whoare we ? 3 Computer Systems Group AGH http://www.icsr.agh.edu.pl/ Knowledge in Grids Team http://www.icsr.agh.edu.pl/index.php/knowledge-in-grids-team Close collaboration with ACC Cyfronet AGH

  4. Whatare we doing ? 4 • Knowledge supported systems for Virtual Organizations • Framework for Intelligent Virtual Organizations • Grid Organizational Memory • X2R • Data monitoring and management • Service Level Agreement Monitoring • QStorMan • Self-scalable systems • Scalarm

  5. A short introduction to data farming 5 Processinitialization

  6. What problem do we address with Scalarm ? 6 Data farming experiments with an exploratory approach Parameter space generation with support of design of experiment methods Accessing heterogeneous computational infrastructure Self-scalability of the management part

  7. Typical work flow 7 2. A workstation or an institution server 3. National Grid environment 1. Local development 4. Cloud

  8. Whereis the problem ? 8 What about fault tolerence? What about collecting results ? 3. Shell + scheduler (gLite, QCG ...) What about parameter space ? 2. Shell Integrated DevelopmentEnvironment 4. Shell + proprietary software

  9. What is our solution ? 9 Specify application to run Execute simulation

  10. Scalarm overview 10 Self-scalable platform adapting to experiment size and simulation type Exploratory approach for conducting experiments Supporting online analysis of experiment partial results Integrates with clusters, Grids, Clouds

  11. Use case – Complete platform for data farming 11

  12. Use case – Simulation-as-a-Service 12 Nativeapp Execute simulation API

  13. But, doesitscale ? 13 Experiment evaluation: • Experiment size [#simulations]: 100 000, 200 000, 500 000, 1 000 000, 2 000 000 • Computational resources [#servers]: 2, 4, 8, 16 • #Clients: 240 * (#servers / 2)

  14. Results 14

  15. Do you want to now more ? 15 Talk to us during the conference Contact us -> dkrol@agh.edu.pl Visit a website -> http://www.scalarm.com

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