Coflex: Navigating the fairness-efficiency tradeoff for coflow scheduling
Why this work is in the frame
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Bibliographic record
Abstract
Fair and efficient coflow scheduling improves application-level networking performance in today's datacenters. Ideally, a coflow scheduler should provide isolation guarantees on the minimum coflow progress to achieve predictable networking performance. Network operators, on the other hand, strive to decrease the average coflow completion time (CCT). Unfortunately, optimal isolation guarantees and minimum average CCT are conflicting objectives and cannot be achieved at the same time. Existing coflow schedulers either optimize isolation guarantees at the expense of long CCTs (e.g., HUG [1]), or decrease the average CCT without performance isolation (e.g., Varys and Aalo [2], [3]). The lack of a smooth tradeoff in between poses a dilemma between low efficiency and no performance isolation. To bridge this gap, we develop a new coflow scheduler, Coflex, to navigate this tradeoff. Coflex allows network operators to specify the desired level of isolation guarantee using a tunable fairness knob, while at the same time decreasing the average CCT. Both our real-world deployments and trace-driven simulations have shown that Coflex offers a smooth tradeoff between fairness and efficiency. At an appropriate tradeoff level, Coflex outperforms fair schedulers by 2 × in minimizing the average CCT.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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