ProvDeploy: Provenance-oriented Containerization of High Performance Computing Scientific Workflows
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914727302529024 |
|---|---|
| author | Kunstmann, Liliane Pina, Débora de Oliveira, Daniel Mattoso, Marta |
| author_facet | Kunstmann, Liliane Pina, Débora de Oliveira, Daniel Mattoso, Marta |
| contents | Many existing scientific workflows require High Performance Computing environments to produce results in a timely manner. These workflows have several software library components and use different environments, making the deployment and execution of the software stack not trivial. This complexity increases if the user needs to add provenance data capture services to the workflow. This manuscript introduces ProvDeploy to assist the user in configuring containers for scientific workflows with integrated provenance data capture. ProvDeploy was evaluated with a Scientific Machine Learning workflow, exploring containerization strategies focused on provenance in two distinct HPC environments |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15324 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ProvDeploy: Provenance-oriented Containerization of High Performance Computing Scientific Workflows Kunstmann, Liliane Pina, Débora de Oliveira, Daniel Mattoso, Marta Distributed, Parallel, and Cluster Computing Databases Many existing scientific workflows require High Performance Computing environments to produce results in a timely manner. These workflows have several software library components and use different environments, making the deployment and execution of the software stack not trivial. This complexity increases if the user needs to add provenance data capture services to the workflow. This manuscript introduces ProvDeploy to assist the user in configuring containers for scientific workflows with integrated provenance data capture. ProvDeploy was evaluated with a Scientific Machine Learning workflow, exploring containerization strategies focused on provenance in two distinct HPC environments |
| title | ProvDeploy: Provenance-oriented Containerization of High Performance Computing Scientific Workflows |
| topic | Distributed, Parallel, and Cluster Computing Databases |
| url | https://arxiv.org/abs/2403.15324 |