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Fixed Time Quanta (FTQ)
The FTQ benchmark takes a time based approach, rather than a workload based, approach to analyzing deterministic processing behavior of operating systems. The time based approach allows for more advanced analysis of the results than workload based benchmarks. For example, with results in the format of "units of work per fixed time period" rather than "amount of time per fixed workload", the analysis can examine the frequency domain and point to periodic problems.
The output if FTQ is raw data in two files, containing time in one and corresponding units of work in the other. Any number of scientific analysis packages can be used to visualize and interpret these results. I have put together a frequency domain visualization tool which plots the time plot and the fast fourier transform of a pair of output files. It uses python, numpy, pylab, and scipy, to present interactive plots.
I am certainly not a Signals and Systems expert, I'd welcome any input on the approaches taken to portray the frequency domain of the results. In particular, the interpolation approach I've taken to ensure the sampling period is uniform is something I would like some expert feedback on.
- python (debian package: python)
- numpy (debian package: python-numeric)
- scipy scipy (debian package: python-scipy)
- pylab (debian package: python-matplotlib)