An analytical model for fair rate calculation in resilient packet rings
Bibliographic record
Abstract
Resilient packet ring (RPR) is a new medium access control (MAC) protocol for high-speed ring networks. It supports spatial reuse and, therefore, maintaining fairness among different nodes is a challenging task in RPR. To ensure fairness among nodes, a fairness algorithm is employed at each RPR node. In case of congestion, the fairness algorithm advertises a fair rate to all upstream nodes contributing to the congestion. In this paper, we develop an analytical model for fair rate calculation in the standard RPR fairness algorithm in the parking lot scenario. We first ignore the link propagation delay and model the system using a nonlinear discrete-time low-pass filter. We, then, consider the link propagation delay and develop a more realistic model. We verify our model by simulation results and analyze the effect of various parameters on the convergence time. Finally, we determine the low-pass filter coefficient to ensure that convergence time of the algorithm is within its minimum range
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".