A Novel Control Theoretic Model for Resilient Packet Ring (RPR) Fairness
Bibliographic record
Abstract
Resilient Packet Ring (RPR) is a new Data Link Layer ring protocol. In RPR, the ring is a shared medium for multiple nodes compete to get a portion of shared bandwidth. Fairness algorithm is responsible for allocating fair bandwidth among competing nodes. In our research, we address the stability problems of the current RPR Fairness and introduce a new solution. The present work is the first control theoretic approach to RPR Fairness and Congestion Control that rigorously models the dynamics of RPR Fairness algorithm by using control theory. The key idea is to involve the active nodes in the Fairness and Queue Congestion Control process which means developing a decentralized control system. In RPR, when the number of nodes or the distance between the RPR nodes is high, the delay plays an important role in the behavior of the fairness which may lead to oscillation, instability and packet loss. We propose the implementation of Smith predictor as a valuable technique to overcome the effects of this delay and achieve higher throughput. Our new theoretical insights allow us to design fairness and congestion control algorithms that achieve fair bandwidth allocation and high throughput with small buffer requirement even in presence of large delay and large number of active nodes in the ring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".