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The increasing availability of cloud computing services for science haschanged the way scientific code can be developed, deployed, and run. Manymodern scientific workflows are capable of running on cloud computingresources. Consequently, there is an increasing interest in the scientificcomputing community in methods, tools, and implementations that enable movingan application to the cloud and simplifying the process, and decreasing thetime to meaningful scientific results. In this paper, we have applied theconcepts of containerization for portability and multi-cloud automateddeployment with industry-standard tools to three scientific workflows. We showhow our implementations provide reduced complexity to portability of both theapplications themselves, and their deployment across private and public clouds.Each application has been packaged in a Docker container with its dependenciesand necessary environment setup for production runs. Terraform and Ansible havebeen used to automate the provisioning of compute resources and the deploymentof each scientific application in a Multi-VM cluster. Each application has beendeployed on the AWS and Aristotle Cloud Federation platforms. Variation in datamanagement constraints, Multi-VM MPI communication, and embarrassingly parallelinstance deployments were all explored and reported on. We thus present asample of scientific workflows that can be simplified using the tools and ourproposed implementation to deploy and run in a variety of cloud environments.

Reproducible and Portable Workflows for Scientific Computing and HPC in the Cloud