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

TL;DR

This paper demonstrates how containerization and automated multi-cloud deployment tools can simplify running scientific workflows across diverse cloud platforms, enhancing portability and reducing complexity.

Contribution

It introduces a practical approach combining Docker, Terraform, and Ansible for deploying scientific workflows on multiple cloud providers, improving portability and ease of deployment.

Findings

Containers enable application portability across clouds.

Automated deployment reduces setup complexity.

Workflows successfully run on AWS and Aristotle Cloud.

Abstract

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.