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How Workflow Engines Should Talk to Resource Managers: A Proposal for a Common Workflow Scheduling Interface

Fabian Lehmann, Jonathan Bader, Friedrich Tschirpke, Lauritz Thamsen, Ulf LeserPublished May 1, 2023
DOI Publisher
Researcher verdict
Context only
Use as context only
Benchmark evidence
Missing
Not verified yet
Time to first repro
A few days
Plan setup time
Risk flags
1
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Abstract

Domain fit: AI-adjacent · Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.

Scientific workflow management systems (SWMSs) and resource managers together ensure that tasks are scheduled on provisioned resources so that all dependencies are obeyed, and some optimization goal, such as makespan minimization, is achieved. In practice, however, there is no clear separation of scheduling responsibilities between an SWMS and a resource manager because there exists no agreed-upon separation of concerns between their different components. This has two consequences. First, the lack of a standardized API to exchange scheduling information between SWMSs and resource managers hinders portability. It incurs costly adaptations when a component should be replaced by a different one (e.g., an SWMS with another SWMS on the same resource manager). Second, due to overlapping functionalities, current installations often actually have two schedulers, both making partial scheduling decisions under incomplete information, leading to suboptimal workflow scheduling. In this paper, we propose a simple REST interface between SWMSs and resource managers, which allows any SWMS to pass dynamic workflow information to a resource manager, enabling maximally informed scheduling decisions. We provide an implementation of this API as an example, using Nextflow as an SWMS and Kubernetes as a resource manager. Our experiments with nine real-world workflows show that this strategy reduces makespan by up to 25.1% and 10.8% on average compared to the standard Nextflow/Kubernetes configuration. Furthermore, a more widespread implementation of this API would enable leaner code bases, a simpler exchange of components of workflow systems, and a unified place to implement new scheduling algorithms.

Results and benchmarks

Freshness tier: cold
Scientific workflow management systems (SWMSs) and resource managers together ensure that tasks are scheduled on provisioned resources so that all dependencies are obeyed, and some optimization goal, such as makespan minimization, is achieved.

Implementation

No direct implementation yet

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Implementation evidence summary
Confidence: low

aws-samples/aws-codepipeline-stepfunctions is the closest maintained adjacent implementation (Matches contextual method/domain keyword: workflow). It is not paper-verified; validate algorithm and evaluation setup against the paper before trusting reported metrics. Community adoption signal: 87 GitHub stars.

Reproduction risks
  • Adjacent implementations are not paper-verified
  • Recommended repository is adjacent and not paper-verified.
  • Adjacent implementation match confidence is low.

Reproduction readiness

Time to first repro: days
Last checked: Aug 24, 2026

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No verified implementation available

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Hardware requirements

  • Expect multi-day setup/compute for meaningful reproduction based on current guidance.

Repositories and ecosystem

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Hugging Face artifacts

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Research context

13

Citations

47

References

Tasks

Computer science, Workflow, Scheduling (production processes), Job shop scheduling, Distributed computing, Software portability, Workflow management system, Workflow technology

Methods

None detected

Domains

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