M <scp>etis</scp> : A Non-Clairvoyant, Workflow-Aware OS Scheduler for Serverless Applications
Bibliographic record
Abstract
Serverless workflows introduce unique challenges for modern cluster schedulers, as they consist of highly concurrent and ephemeral functions with unpredictable execution patterns. Through analysis of workloads derived from production serverless trace characteristics, we observe that existing OS-level schedulers, such as Linux CFS, lack workflow-level awareness and make scheduling decisions solely at the function level, which can result in bottlenecks within workflows and prolonged Workflow Completion Times (WCTs). We present Metis, anon-clairvoyant, workflow-aware OS scheduler designed specifically for serverless workflow workloads, which aims to reduce average WCTs by treating workflows as first-class scheduling entities. Metis implements Workflow-Aware Least-Attained Service (WLAS), a non-clairvoyant scheduling algorithm that leverages workflow-level virtual clocks and critical path estimation to reduce WCTs and ensure fairness. By utilizing eBPF to hook into existing OS primitives, Metis achieves practical deployment with minimal kernel modifications. Extensive synthetic trace-driven simulations demonstrate that Metis reduces average WCTs by 31.3% and the 95th percentile by 14.3% compared to state-of-the-art function-centric scheduling approaches. Real-system experiments across diverse workflow patterns show improvements ranging from 47.2% to 58.2% in average end-to-end latency and the 99th percentile by up to 73.9% over baseline schedulers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".