  Scientific workflow applications have become mainstream and their automated
and efficient execution on large-scale compute platforms is the object of
extensive research and development. For these efforts to be successful, a solid
experimental methodology is needed to evaluate workflow algorithms and systems.
A foundation for this methodology is the availability of realistic workflow
instances. Dozens of workflow instances for a few scientific applications are
available in public repositories. While these are invaluable, they are limited:
workflow instances are not available for all application scales of interest. To
address this limitation, previous work has developed generators of synthetic,
but representative, workflow instances of arbitrary scales. These generators
are popular, but implementing them is a manual, labor-intensive process that
requires expert application knowledge. As a result, these generators only
target a handful of applications, even though hundreds of applications use
workflows in production.
  In this work, we present WfChef, a framework that fully automates the process
of constructing a synthetic workflow generator for any scientific application.
Based on an input set of workflow instances, WfChef automatically produces a
synthetic workflow generator. We define and evaluate several metrics for
quantifying the realism of the generated workflows. Using these metrics, we
compare the realism of the workflows generated by WfChef generators to that of
the workflows generated by the previously available, hand-crafted generators.
We find that the WfChef generators not only require zero development effort
(because it is automatically produced), but also generate workflows that are
more realistic than those generated by hand-crafted generators.
