PROV-IO: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze four representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO <tex-math notation="LaTeX">⁺</tex-math> , which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO <tex-math notation="LaTeX">⁺</tex-math> to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO <tex-math notation="LaTeX">⁺</tex-math> framework can support both containerized and non-containerized workflows on different HPC platforms with flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO <tex-math notation="LaTeX">⁺</tex-math> can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO <tex-math notation="LaTeX">⁺</tex-math> outperforms a state-of-the-art system (i.e., ProvLake) in our experiments.