  The increasing availability of cloud computing services for science has
changed the way scientific code can be developed, deployed, and run. Many
modern scientific workflows are capable of running on cloud computing
resources. Consequently, there is an increasing interest in the scientific
computing community in methods, tools, and implementations that enable moving
an application to the cloud and simplifying the process, and decreasing the
time to meaningful scientific results. In this paper, we have applied the
concepts of containerization for portability and multi-cloud automated
deployment with industry-standard tools to three scientific workflows. We show
how our implementations provide reduced complexity to portability of both the
applications themselves, and their deployment across private and public clouds.
Each application has been packaged in a Docker container with its dependencies
and necessary environment setup for production runs. Terraform and Ansible have
been used to automate the provisioning of compute resources and the deployment
of each scientific application in a Multi-VM cluster. Each application has been
deployed on the AWS and Aristotle Cloud Federation platforms. Variation in data
management constraints, Multi-VM MPI communication, and embarrassingly parallel
instance deployments were all explored and reported on. We thus present a
sample of scientific workflows that can be simplified using the tools and our
proposed implementation to deploy and run in a variety of cloud environments.
