Reproducibility of execution environments in computational science using Semantics and Clouds

Abstract

In the past decades, one of the most common forms of addressing reproducibility in scientific workflow-based computational science has consisted of tracking the provenance of the produced and published results. Such provenance allows inspecting intermediate and final results, improves understanding, and permits replaying a workflow execution. Nevertheless, this approach does not provide any means for capturing and sharing the very valuable knowledge about the experimental equipment of a computational experiment, i.e., the execution environment in which the experiments are conducted. In this work, we propose a novel approach based on semantic vocabularies that describes the execution environment of scientific workflows, so as to conserve it. We define a process for documenting the workflow application and its related management system, as well as their dependencies. Then we apply this approach over three different real workflow applications running in three distinct scenarios, using public, private, and local Cloud platforms. In particular, we study one astronomy workflow and two life science workflows for genomic information analysis. Experimental results show that our approach can reproduce an equivalent execution environment of a predefined virtual machine image on all evaluated computing platforms.

Adaptive and reflective middleware for the cloudification of simulation & optimization workflows

“…In that regard, Santana-Perez et al [12] describe the execution environment of workflows using semantic vocabularies to produce annotated workflows (i.e. logical preservation of execution environment).…”

A framework for scientific workflow reproducibility in the cloud

“…Santana-Perez et al proposed in [9] a semantic-based approach to preserve workflows with their execution environment. They use a set of semantic vocabularies to specify the resources involved in the execution of a workflow.…”

“…Currently, most of the approaches that address reproducibility of scientific workflows have focused either on their physical preservation, in which a workflow is conserved by packaging all of its components, so an identical replica is created and can be reused; or on logical preservation, in which the workflow and its components are described with enough information for others to reproduce a similar workflow in the future [9].…”

JobPruner: A machine learning assistant for exploring parameter spaces in HPC applications

“…High Performance Computing (HPC) simulations used in these fields produce plenty of data that could be used to optimize new experiments. Such data is becoming even more pervasive due to the increasing efforts in improving reproducibility of computational experiments [1], [2], [3]. Natural questions are: what is the actual benefit of exploiting data from previously executed experiments and how to do that properly.…”