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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 con- sider 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.
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ExoFlow: A Universal Workflow System for Exactly-Once DAGs
Given the fundamental tradeoff between run-time and recovery performance, current distributed systems often build application-specific recovery strategies to minimize overheads. However, it is increasingly common for different applications to be composed into heterogeneous pipelines. Implementing multiple interoperable recovery techniques in the same system is rare and difficult. Thus, today's users must choose between: (1) building on a single system, and face a fixed choice of performance vs. recovery overheads, or (2) the challenging task of stitching together multiple systems that can offer application-specific tradeoffs. We present ExoFlow, a universal workflow system that enables a flexible choice of recovery vs. performance tradeoffs, even within the same application. The key insight behind our solution is to decouple execution from recovery and provide exactly-once semantics as a separate layer from execution. For generality, workflow tasks can return references that capture arbitrary inter-task communication. To enable the workflow system and therefore the end user to take control of recovery, we design task annotations that specify execution semantics such as nondeterminism. ExoFlow generalizes recovery for existing workflow applications ranging from ETL pipelines to stateful serverless workflows, while enabling further optimizations in task communication and recovery.
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- Award ID(s):
- 1730628
- PAR ID:
- 10523930
- Publisher / Repository:
- USENIX Association
- Date Published:
- ISBN:
- 978-1-939133-34-2
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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