Efficient Integration of Containers into Scientific Workflows

Abstract

Containers offer a powerful way to create portability for scientific applications. However yet incorporating them into workflows requires careful consideration, as straightforward approaches can increase network usage and runtime. We identified three issues in this process: container composition, containerizing workers or jobs, and container image translation. To tackle composition, we define data into three types: OS data, Read-Only, andWorking data, and define dynamic and static composition. Using the static composition (creating a single container for each job) leads to massive waste in sending duplicate data over the network. Dynamic composition (sending the data types separately) enables caching on worker nodes. To answer running workers or jobs inside a container, we looked at the costs of running inside of a container. Finally, when using different types of container technologies simultaneously, we found it's better to convert to the target image types before sending the container images, instead of repeating the same conversion at the job nodes, leading to more wasted time.

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