Perfect is the Enemy of Good: Lloyd-Max Quantization for Rate Allocation in Congestion Control Plane
Bibliographic record
Abstract
Decoupling congestion control plane from datapath can expedite the development of new congestion control solutions. It also creates opportunities for explicit rate allocation schemes. Dealing with large numbers of flows remains a major challenge. Max-min fairness – the gold standard for flow rate allocation – has a running complexity proportional to the number of flows, which might be prohibitive in large-scale networks.To accelerate explicit rate allocation, we present solutions using rate quantization, i.e. mapping the continuous range of flow rates to a small number of bins. We use Lloyd-Max, a quantization method that generates bins according to the distribution of flow rates, to dynamically adjust the quantization bins over time. Our experimental evaluation shows that the distortion caused by this quantization scheme is small, and can be negligible compared to intrinsic errors in measuring and enforcing rates in current solutions.We also show that rate quantization can significantly speed up max-min fair rate allocation, reducing the run-time by 70 − 95%. Besides, Lloyd-Max quantization using recent history of flow rates performs close to the case when we have access to the exact current (or future) rates. This is an interesting observation as it obviates the need for complex techniques that try to predict future rates.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".