Coflex: Navigating the fairness-efficiency tradeoff for coflow scheduling
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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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".