Modeling and Analysis of Fair Rate Calculation in Resilient Packet Ring Conservative Mode
Bibliographic record
Abstract
Resilient Packet Ring (RPR), which is standardized as IEEE 802.17, is a new Medium Access Control (MAC) protocol for high-speed metro-area ring networks. RPR supports spatial reuse and, therefore, maintaining fairness among different nodes in accessing the ring bandwidth is a challenging task in RPR. In order to achieve fairness among nodes, a fairness algorithm is employed at each RPR node. When a node is not given access to the ring, it becomes congested. In this case, the congested node calculates a fair rate which is advertised to all upstream nodes contributing to congestion. When upstream nodes receive the control message, they limit the rate of their injected traffic to the advertised fair rate. Consequently, the congested node will be able to add its traffic to the ring. In this paper, we develop an analytical model for fair rate calculation in the standard RPR Conservative Mode fairness algorithm in the parking-lot scenario. This model can be used to evaluate the performance of the fairness algorithm. We investigate this problem in two cases. First, we assume that the link propagation delay is zero. Then, we consider the link propagation delay and develop a more realistic model. The fair rate equations are derived for both cases. We verify the accuracy of our model by simulation results. Furthermore, we use the developed model to study the impact of various parameters on convergence of the fair rate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".