  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 at 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
work, we have presented a collection of tools that have been used for aiding
research and development activities in the Pegasus project, and that have been
adopted by others for conducting workflow research. Despite their popularity,
there are several shortcomings that prevent easy adoption, maintenance, and
consistency with the evolving structures and computational requirements of
production workflows. In this work, we present WorkflowHub, a community
framework that provides a collection of tools for analyzing workflow execution
traces, producing realistic synthetic workflow traces, and simulating workflow
executions. We demonstrate the realism of the generated synthetic traces by
comparing simulated executions of these traces with actual workflow executions.
We also contrast these results with those obtained when using the previously
available collection of tools. We find that our framework not only can be used
to generate representative synthetic workflow traces (i.e., with workflow
structures and task characteristics distributions that resembles those in
traces obtained from real-world workflow executions), but can also generate
representative workflow traces at larger scales than that of available workflow
traces.
