Novel bandwidth allocation scheme for ring networks with spatial reuse
Bibliographic record
Abstract
Spatial Reuse can significantly increase the throughput of optical ring networks by allowing multiple stations to transmit concurrently over distinct segments of the ring. Buffer Insertion Ring (BIR) scheme is widely used to achieve spatial reuse. However, because non-preemptive priority is usually given to the ring (pass-through) traffic, BIR scheme may cause fairness problems in allocating the ring bandwidth among distinct nodes. In this paper, we propose a novel approach that can prevent starvation and maximize the throughput with low complexity. The main idea of this method is that for every node of the ring to provide a separate queue for each source that shares the output link of the particular node. We then fairly allocate the output link bandwidth to all the sources based on the weight of the corresponding queues. Simulations and analysis show that this new scheme can provide fairness with less end-to-end delay compare to BIR scheme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".