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
- NSF-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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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
