A Framework for the Preservation of a Docker Container





Reliably building and maintaining systems across environments is a continuing problem. A project or experiment may run for years. Software and hardware may change as can the operating system. Containerisation is a technology that is used in a variety of companies, such as Google, Amazon and IBM, and scientific projects to rapidly deploy a set of services repeatably. Using Dockerfiles to ensure that a container is built repeatably, to allow conformance and easy updating when changes take place are becoming common within projects. Its seen as part of sustainable software development. Containerisation technology occupies a dual space: it is both a repository of software and software itself. In considering Docker in this fashion, we should verify that the Dockerfile can be reproduced. Using a subset of the Dockerfile specification, a domain specific language is created to ensure that Docker files can be reused at a later stage to recreate the original environment. We provide a simple framework to address the question of the preservation of containers and its environment. We present experiments on an existing Dockerfile and conclude with a discussion of future work. Taking our work, a pipeline was implemented to check that a defined Dockerfile conforms to our desired model, extracts the Docker and operating system details. This will help the reproducibility of results by creating the machine environment and package versions. It also helps development and testing through ensuring that the system is repeatably built and that any changes in the software environment can be equally shared in the Dockerfile. This work supports not only the citation process it also the open scientific one by providing environmental details of the work. As a part of the pipeline to create the container, we capture the processes used and put them into the W3C PROV ontology. This provides the potential for providing it with a persistent identifier and traceability of the processes used to preserve the metadata. Our future work will look at the question of linking this output to a workflow ontology to preserve the complete workflow with the commands and parameters to be given to the containers. We see this provenance within the build process useful to provide a complete overview of the workflow.






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