Deploying High Throughput Scientific Workflows on Container Schedulers with Makeflow and Mesos

Workflows are a widely used abstraction for describing large scientific applications and running them on distributed systems. However, most workflow systems have been silent on the question of what execution environment each task in the workflow is expected to run in. Consequently, a workflow may run successfully in the environment it was created, but fail on other platforms due to the differences in execution environment. Container-based schedulers have recently arisen as a potential solution to this problem, adopting containers to distribute computing resources and deliver well-defined execution environments to applications. In this paper, we consider how to connect workflow system to container schedulers with minimal performance loss and higher system efficiency. As an example of current technology, we use Makeflow and Mesos. We present five design challenges, and address them by using four configurations that connecting workflow system to container scheduler from different level of the infrastructure. In order to take full advantage of the resource sharing schema of Mesos, we enable the resource monitor of Makeflow to dynamically update the task resource requirement. We explore the performance of a large bioinformatics workflow, and observe that using Makeflow, Work Queue and the Resource monitor together not only increase the transfer throughput but also achieves highest resource usage rate.