Virtual queuing: an efficient algorithm for bandwidth management in resilient packet rings
Bibliographic record
Abstract
Resilient packet ring (RPR) is being devised as part of IEEE 802.17 standard, where fairness in bandwidth allocation among ring nodes, efficiency in resource utilization, and a low computational complexity are the main requirements. Although recent efforts have improved the performance of the RPR fairness algorithms to have acceptable steady-state behavior, we demonstrate that current algorithms suffer from extreme unfairness and throughput loss in some dynamic traffic scenarios. In this paper, we address the bandwidth management in RPR. First, we propose a general fairness model for packet rings. Then, a new algorithm for bandwidth management in RPR called virtual queuing (VQ) is introduced. We study the fairness properties of VQ algorithm both analytically and with simulation results. Compared to the RPR standard fairness algorithms that suffer from a throughput loss of up to 28% in some cases, the throughput loss with VQ is less than 2%. Comparing to another algorithm, called distributed virtual-time scheduling in rings (DVSR), VQ has a lower computational complexity and a better performance in a dynamic traffic environment. We show that the average throttled rate of the head node in a congestion span can be up to 80% for DVSR With VQ, it is less than 4% in all cases.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 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".