Scheduling latency-critical traffic: a measurement study of DRR+ and DRR++
Bibliographic record
Abstract
Efficient fair queuing using deficit round-robin, DRR, proposed by Shreedhar and Varghese (1996) is a low-complexity packet scheduler that has several commercial implementations. DRR has also been extended as DRR+ to accommodate latency-critical flows. DRR+, however, assumes that a latency-critical flow exhibits very smooth arrivals whereas most network flows are very bursty in nature, either as the result of source bursts, or as a result of the dynamics of multihop network paths. When DRR+ encounters a burst, it reverts back to the behavior of DRR, providing no preference or latency bound for latency critical traffic. This is a fatal flaw that prevents DRR+ from being useful in scheduling bursty latency-critical flows. We present a different extension to DRR that has much lower delay and delay jitter than DRR+ and is capable of handling bursty latency-critical flows.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".