A Semantic-Based Approach to Attain Reproducibility of Computational Environments in Scientific Workflows: A Case Study

Abstract

AbstractReproducible research in scientific workflows is often addressed by tracking the provenance of the produced results. While this approach allows inspecting intermediate and final results, improves understanding, and permits replaying a workflow execution, it does not ensure that the computational environment is available for subsequent executions to reproduce the experiment. In this work, we propose describing the resources involved in the execution of an experiment using a set of semantic vocabularies, so as to conserve the computational environment. We define a process for documenting the workflow application, management system, and their dependencies based on 4 domain ontologies. We then conduct an experimental evaluation using a real workflow application on an academic and a public Cloud platform. Results show that our approach can reproduce an equivalent execution environment of a predefined virtual machine image on both computing platforms.KeywordsVirtual MachineSoftware ComponentDomain OntologySemantic AnnotationInfrastructure ProviderThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.