Submitted:
30 January 2020
Posted:
31 January 2020
You are already at the latest version
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.
Keywords:
workflows
; containers
; cloud computing
; Kubernetes
; big data
; reproducibility
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
