Version 1
: Received: 30 January 2020 / Approved: 31 January 2020 / Online: 31 January 2020 (05:15:01 CET)
How to cite:
Spjuth, O.; Capuccini, M.; Carone, M.; Larsson, A.; Schaal, W.; Novella, J.A.; Stein, O.; Ekmefjord, M.; Di Tommaso, P.; Floden, E.; Notredame, C.; Moreno, P.; Emami Khoonsari, P.; Herman, S.; Kultima, K.; Lampa, S. Approaches for Containerized Scientific Workflows in Cloud Environments with Applications in Life Science. Preprints2020, 2020010378 (doi: 10.20944/preprints202001.0378.v1).
Spjuth, O.; Capuccini, M.; Carone, M.; Larsson, A.; Schaal, W.; Novella, J.A.; Stein, O.; Ekmefjord, M.; Di Tommaso, P.; Floden, E.; Notredame, C.; Moreno, P.; Emami Khoonsari, P.; Herman, S.; Kultima, K.; Lampa, S. Approaches for Containerized Scientific Workflows in Cloud Environments with Applications in Life Science. Preprints 2020, 2020010378 (doi: 10.20944/preprints202001.0378.v1).
Cite as:
Spjuth, O.; Capuccini, M.; Carone, M.; Larsson, A.; Schaal, W.; Novella, J.A.; Stein, O.; Ekmefjord, M.; Di Tommaso, P.; Floden, E.; Notredame, C.; Moreno, P.; Emami Khoonsari, P.; Herman, S.; Kultima, K.; Lampa, S. Approaches for Containerized Scientific Workflows in Cloud Environments with Applications in Life Science. Preprints2020, 2020010378 (doi: 10.20944/preprints202001.0378.v1).
Spjuth, O.; Capuccini, M.; Carone, M.; Larsson, A.; Schaal, W.; Novella, J.A.; Stein, O.; Ekmefjord, M.; Di Tommaso, P.; Floden, E.; Notredame, C.; Moreno, P.; Emami Khoonsari, P.; Herman, S.; Kultima, K.; Lampa, S. Approaches for Containerized Scientific Workflows in Cloud Environments with Applications in Life Science. Preprints 2020, 2020010378 (doi: 10.20944/preprints202001.0378.v1).
Abstract
Containers are gaining popularity in life science research as they provide a solution for encompassing dependencies of provisioned tools, simplify software installations for end users and offer a form of isolation between processes. Scientific workflows are ideal for chaining containers into data analysis pipelines to aid in creating reproducible analyses. In this manuscript we review a number of approaches to using containers as implemented in the workflow tools Nextflow, Galaxy, Pachyderm, Argo, Kubeflow, Luigi and SciPipe, when deployed in cloud environments. A particular focus is placed on the workflow tool’s interaction with the Kubernetes container orchestration framework.
workflows; containers; cloud computing; Kubernetes; big data; reproducibility
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Ola Spjuth
,