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Classification of scientific workflows based on reproducibility analysis

In the scientist's community one of the most vital challenges is the reproducibility of a workflow execution. The necessary parameters of the execution (we call them descriptors) can be external which depend on for example the computing infrastructure (grids, clusters and clouds), on third party resources or it can be internal which belong to the code of the workflow such as variables. Consequently, during the process of re-execution these parameters may change or become unavailable and finally they can prevent to reproduce the workflow. However in most cases the lack of the original parameters can be compensated by replacing, evaluating or simulating the value of the descriptors with some extra cost in order to make it reproducible. Our goal in this paper is to classify the scientific workflows based on the method and cost how they can become reproducible.