Workflow systems are in wide use in the scientific community today, facilitating complex computational and analytical processes. Their increasing popularity is particularly visible at workflow sharing sites such as MyExperiment [1] or Galaxy [2-4]. High-performance computing (HPC) users also are looking toward workflow solutions to manage their complex preand post-processing needs. This trend likely will continue with the advent of exascale architectures, which will require extreme-scale collaborations between applications running on an exascale system and community data and knowledge repositories needed for their validation and steering [5, 6]. A new emerging use for workflows is the in-situ / streaming, often adaptive analysis of large scale simulation runs and as well as the need to analyze and interpret experimental results [10], in both cases to steer the scientific work and optimize the scientific outcome. In particular in this last case the reliably performance of the workflow is absolutely key to its usefulness.
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