A Study of Orchestration Approaches for Scientific Workflows in Serverless Computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scientific workflows are typically data- and compute-intensive. They consist of many stages, each of which may contain hundreds to even thousands of tasks. Traditionally, scientific workflows have been executed using the serverful computing model. Serverless computing presents an attractive alternative to the serverful computing model as it frees developers from managing and provisioning resources and offers a fine-grained pay-as-you-go pricing model. In this paper, we investigate the viability of using serverless computing to execute scientific workflows. Specifically, we discuss, implement, and evaluate three orchestration approaches for executing scientific workflows: serverful-centralized, serverless-centralized, and serverless-decentralized. This work is the first to implement and evaluate a purely serverless orchestration approach that does not require deploying a dedicated workflow manager. Our evaluation shows that serverless orchestration approaches cause a noticeable performance overhead for some workflow patterns (e.g., reduce stages) due to accessing a large amount of remote data. We propose two optimizations (i.e., prefetching file privileges and container placement) that exploit data locality to mitigate that impact. Our evaluation with the Montage application shows that a fully decentralized approach achieves a comparable performance to a serverful approach. Also, our results show that prefetching file privileges and container placement optimizations improve the performance by 26% and 44% respectively when compared to an unoptimized version.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it