The Bounded Data Reuse Problem in Scientific Workflows

Large datasets and time-consuming processes have become the norm in scientific computing applications. The exploration phase in the development of scientific workflows involves trial-and-error with workflow components, which can take a lot of time given the time-consuming nature of the workflow tasks. These facts suggest the possibility of reducing the development time by reusing intermediate data whenever possible. However the storage space is always limited. This introduces a problem: which intermediate datasets from one workflow should be kept to be reused in another workflow, with a limited amount of storage. For the general class of series parallel graphs, we model this problem using a non-linear integer programming formulation and show that it is NP-Hard. We provide a branch and bound optimal algorithm as well as efficient heuristics. We conducted experiments over a large set of randomly-generated workflows as well as a smaller set of synthetic workflows which are based on real-world workflows used by scientists in different disciplines. Our experiments show that the best solution produced by the heuristics only differs from the optimal value by less than 1% on average.