Reproducibility of Computational Experiments on Kubernetes-Managed Container Clouds with HyperFlow

We propose a comprehensive solution for reproducibility of scientific workflows. We focus particularly on Kubernetes-managed container clouds, increasingly important in scientific computing. Our solution addresses conservation of the scientific procedure, scientific data, execution environment and experiment deployment, while using standard tools in order to avoid maintainability issues that can obstruct reproducibility. We introduce an Experiment Digital Object (EDO), a record published in an open science repository that contains artifacts required to reproduce an experiment. We demonstrate a variety of reproducibility scenarios including experiment repetition (same experiment and conditions), replication (same experiment, different conditions), and propose a smart reuse scenario in which a previous experiment is partially replayed and partially re-executed. The approach is implemented in the HyperFlow workflow management system and experimentally evaluated using a genomic scientific workflow. The experiment is published as an EDO record on the Zenodo platform.

[1]  Ladislav Hluchý,et al.  Abstraction Layer for Development and Deployment of Cloud Services , 2012, Comput. Sci..

[2]  Bartosz Balis,et al.  HyperFlow: A model of computation, programming approach and enactment engine for complex distributed workflows , 2016, Future Gener. Comput. Syst..

[3]  María S. Pérez-Hernández,et al.  Reproducibility of execution environments in computational science using Semantics and Clouds , 2017, Future Gener. Comput. Syst..

[4]  Rion Dooley,et al.  Endofday: A Container Workflow Engine for Scalable, Reproducible Computation , 2016, IWSG.

[5]  Douglas Thain,et al.  Deploying High Throughput Scientific Workflows on Container Schedulers with Makeflow and Mesos , 2017, 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[6]  Alban Gaignard,et al.  Scientific workflows for computational reproducibility in the life sciences: Status, challenges and opportunities , 2017, Future Gener. Comput. Syst..

[7]  Ewa Deelman,et al.  Introducing PRECIP: An API for Managing Repeatable Experiments in the Cloud , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[8]  Jens Krüger,et al.  Reproducible Scientific Workflows for High Performance and Cloud Computing , 2019, 2019 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID).

[9]  Idafen Santana-Perez,et al.  Towards Reproducibility in Scientific Workflows: An Infrastructure-Based Approach , 2015, Sci. Program..

[10]  Ian J. Taylor,et al.  Workflows and e-Science: An overview of workflow system features and capabilities , 2009, Future Gener. Comput. Syst..

[11]  Seetharami R. Seelam,et al.  Resource Management for Running HPC Applications in Container Clouds , 2016, ISC.

[12]  Kai Liu,et al.  Flexible Container-Based Computing Platform on Cloud for Scientific Workflows , 2016, 2016 International Conference on Cloud Computing Research and Innovations (ICCCRI).

[13]  Miron Livny,et al.  Pegasus, a workflow management system for science automation , 2015, Future Gener. Comput. Syst..

[14]  Paul Watson,et al.  A framework for scientific workflow reproducibility in the cloud , 2016, 2016 IEEE 12th International Conference on e-Science (e-Science).

[15]  Ryan E. Grant,et al.  Enabling HPC Workloads on Cloud Infrastructure Using Kubernetes Container Orchestration Mechanisms , 2019, 2019 IEEE/ACM International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC).

[16]  Michal Wrzeszcz,et al.  Onedata - a Step Forward towards Globalization of Data Access for Computing Infrastructures , 2015, ICCS.

[17]  Bartosz Balis,et al.  Transparent Deployment of Scientific Workflows across Clouds - Kubernetes Approach , 2018, 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion).