Workflows are a widely accepted and used abstraction for unambiguously representing the right details of any complicated and large-scale business and scientific applications and executing them on distributed computing systems such as clusters, clouds, and grids. However, workflow management systems have been largely evasive in conveying the relevant information of the underlying environment on which the tasks inscribed in the workflow are to run. This means that the workflow tasks can run perfectly on the environment for which they were designed. The real challenge is to run the tasks across multiple IT environments without tweaking and twisting the source codes of the required tasks. Increasingly, the IT environments are heterogeneous with the leverage of disparate operating systems, middleware, programming languages and frameworks, databases, and so on. Typically, workflow systems focus on data interchange between tasks and are environment-specific. A workflow, which is working fine in one environment, starts to crumble when it is being migrated and deployed on different IT environments. All kinds of known and unknown dependencies and incompatibilities spring up to denigrate the workflows delaying the whole job of IT setup, application installation and configuration, deployment, and delivery. Containers are the best bet for resolving this imbroglio once and for all.

In the article, Integrating Containers into Workflows: A Case Study Using Makeflow, Work Queue, and Docker, Chao Zheng and Douglas Thain have done a good job of analyzing several methods in order to experimentally prove the unique contributions of containers in empowering workflow/process management systems. They have explored the performance of a large bioinformatics workload on a Docker-enabled cluster and observed the best configuration to be locally managed on containers that are shared between multiple tasks.