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The last decade has brought tremendous changes to the characteristics of large scale distributed computing platforms. Large grids processing terabytes of information a day and the peer-to-peer technology have become common even though understanding how to efficiently such platforms still raises many challenges. As demonstrated by the USS SimGrid project funded by the ANR in 2008, simulation has proved to be a very effective approach for studying such platforms. Although even more challenging, we think the issues raised by petaflop/exaflop computers and emerging cloud infrastructures can be addressed using similar simulation methodology.

The goal of the SONGS project is to extend the applicability of the SimGrid simulation framework from Grids and Peer-to-Peer systems to Clouds and High Performance Computation systems. Each type of large-scale computing system will be addressed through a set of use cases and lead by researchers recognized as experts in this area.

Any sound study of such systems through simulations relies on the following pillars of simulation methodology: Efficient simulation kernel; Sound and validated models; Simulation analysis tools; Campaign simulation management.

Roadmap

The SONGS project will be organized in eight work packages: one for each application domain and one for each pillar of the simulation methodology.

Application Domains

  • WP1 [Data]Grid
    • Distributed Data mgnt for LHC and Hierarchical Storage System
  • WP2 Peer-to-Peer and Volunteer Computing
    • Replica Placement in VOD, Affinities in VC
  • WP3 Clouds
    • Study from client or provider POV, other metrics (energy)
  • WP4 High Performance Computing
    • Exascale, memory and energy models

Simulation Pillars

  • WP5 Efficient Simulation Kernel
    • Optimization and standardization
  • WP6 Concepts and Models
    • Storage, memory, energy, HPN and volatility
  • WP7 Analysis and Visualization
    • Scalable visualization and trace comparison
  • WP8 Support to Simulation Methodology
    • DoE, campaign management, and Open Science

Partners


SONGS is funded for four years (2012-2015) by the The French National Research Agency (ANR) under contract no. ANR-11-INFRA-13.

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