Four level provenance support to achieve portable reproducibility of scientific workflows

In the scientist's community one of the most vital challenges is the issue of reproducibility of workflow execution. In order to reproduce the results of an experiment, on one hand provenance information must be collected and on the other hand the dependencies of the execution need to be eliminated. Concerning the workflow execution environment we have differentiated four levels of provenance: infrastructural, environmental, workflow and data provenance. During the re-execution at all levels the components can change and capturing the data of each levels targets different problems to solve. For example storing the environmental and infrastructural parameters enables the portability of workflows between the different parallel and distributed systems (grid, HPC, cloud). The describers of the workflow model enable tracking the different versions of the workflow and their impacts on the execution. Our goal is to capture the most optimal parameters in number and type as well and reconstruct the way of data production independently from the environment. In this paper we investigate the necessary and satisfactory parameters of workflow reproducibility and give a mathematical formula to determine the rate of reproducibility. These measurements allow the scientist to make a decision about the next steps toward the creation of reproducible workflows.