Modern scientific collaborations have opened up the opportunity to solve complex problems that require both multidisciplinary expertise and large-scale computational experiments. These experiments typically consist of a sequence of processing steps that need to be executed on selected computing platforms. Execution poses a challenge, however, due to (1) thecomplexity and diversity of applications, (2) thediversity of analysis goals, (3) theheterogeneity of computing platforms, and (4) thevolume and distribution of data. A common strategy to make these in silico experiments more manageable is to model them asworkflowsand to use a workflow management system to organize their execution. This article looks at the overall challenge posed by a new order of scientific experiments and the systems they need to be run on, and examines how this challenge can be addressed by workflows and workflow management systems. It proposes a taxonomy of workflow management system (WMS) characteristics, including aspects previously overlooked. This frames a review of prevalent WMSs used by the scientific community, elucidates their evolution to handle the challenges arising with the emergence of the “fourth paradigm,” and identifies research needed to maintain progress in this area.
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