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Ginpex (Goal-oriented INfrastructure Performance EXperiments) is being developed for

  • Running experiments that induce certain load patterns on machines
    • Use Ginpex as load generator
    • Distribute load to multiple machines
    • Convenient experiment specification with the experiment model editor
    • Add machines dynamically to running experiments
    • Automate experiments
    • Integrate your own experiment logic into Ginpex experiment models
  • Automatically deriving performance-relevant infrastructure parameters
    • Ginpex ships with predefined experiments
    • Use detected parameters for performance prediction, for example as input for the PCM

Infrastructure parameters in focus include

  • OS scheduling parameters (timeslice length, load balancing policies)
  • Resource parameters (HDD, network)
  • Virtualization parameters (Virtualization overhead, ...)

Additional features include

  • A UI editor to comfortably specify experiment specifications
  • Java code generation based on specified experiment specifications for efficient experiment execution on any platform where Java is available
  • Checkpoint functionality (snapshots) for long-running experiments


The main application scenario of Ginpex is embedded in model-based software performance prediction.

Overall Workflow of the Ginpex Approach


The architecture is based on the Performance Cockpit approach [1].

Ginpex Architecture


Metamodel in Detail


Available Ginpex extensions


Ginpex installation instructions

How to use Ginpex

Have a look at the Ginpex Cookbook for help on using Ginpex.

Predefined experiments

sdqinternal:Ginpex/Predefined Experiments

Including Virtualization Overheads in Performance Prediction


Ginpex Homepage

For questions, contact Michael Hauck.