|
Descripción
|
|
|---|---|
| One of the most common forms of addressing reproducibility in scientific workflow-based computational science in the past decades has consisted in 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 for describing the execution environment of scientific workflows, so as to conserve them, using semantic vocabularies. 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 di erent real workflow applications on three distinguished 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. | |
|
Internacional
|
Si |
|
JCR del ISI
|
Si |
|
Título de la revista
|
Future Generation Computer Systems-the International Journal of Escience |
|
ISSN
|
0167-739X |
|
Factor de impacto JCR
|
2,43 |
|
Información de impacto
|
|
|
Volumen
|
|
|
DOI
|
10.1016/j.future.2015.12.017 |
|
Número de revista
|
|
|
Desde la página
|
1 |
|
Hasta la página
|
13 |
|
Mes
|
SIN MES |
|
Ranking
|
|