Interleaved Polling Versus Multi-Thread Polling for Bandwidth Allocation in Long-Reach PONs
Bibliographic record
Abstract
Long-reach passive optical networks (LR-PONs) suffer from extremely long propagation delays that degrade the performance of centralized algorithms proposed for upstream bandwidth allocation in traditional PONs. This is because these algorithms are based on bandwidth negotiation messages frequently exchanged between the optical line terminal in the central office and optical network units near the users, which become seriously delayed when the network is extended causing the performance to degrade. In this paper, we review and analyze two centralized dynamic bandwidth allocation algorithms, online interleaved polling and offline multi-thread polling that was recently proposed in the literature for LR-PONs. We investigate and compare their performances together in detail, by studying and observing their elemental delays. Unexpectedly, simulation results show that, although multi-thread polling succeeds in decreasing reporting and queueing delays, interleaved polling keeps a lower grant delay and therefore has better overall delay performance. The latter also achieves better throughput compared to multi-thread polling.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".