  Scientific workflows are a cornerstone of modern scientific computing. They
are used to describe complex computational applications that require efficient
and robust management of large volumes of data, which are typically
stored/processed on heterogeneous, distributed resources. The workflow research
and development community has employed a number of methods for the quantitative
evaluation of existing and novel workflow algorithms and systems. In
particular, a common approach is to simulate workflow executions. In previous
works, we have presented a collection of tools that have been adopted by the
community for conducting workflow research. Despite their popularity, they
suffer from several shortcomings that prevent easy adoption, maintenance, and
consistency with the evolving structures and computational requirements of
production workflows. In this work, we present WfCommons, a framework that
provides a collection of tools for analyzing workflow executions, for producing
generators of synthetic workflows, and for simulating workflow executions. We
demonstrate the realism of the generated synthetic workflows by comparing their
simulated executions to real workflow executions. We also contrast these
results with results obtained when using the previously available collection of
tools. We find that the workflow generators that are automatically constructed
by our framework not only generate representative same-scale workflows (i.e.,
with structures and task characteristics distributions that resemble those
observed in real-world workflows), but also do so at scales larger than that of
available real-world workflows. Finally, we conduct a case study to demonstrate
the usefulness of our framework for estimating the energy consumption of
large-scale workflow executions.
