On SLA constraints in dynamic bandwidth allocation for long-reach passive optical networks
Bibliographic record
Abstract
Long-Reach Passive Optical Network (LRPON) technology seems to be a strong candidate for next generation optical access networks. LRPON aims to combine the advantages of optical metro and access networks before the backbone by offering long feeder distance and high split ratio. On the other hand, long feeder distance causes long propagation delay while together with high split ratio they lead to longer packet delay. In order to decrease packet delay, we have recently proposed a QoS-aware bandwidth allocation scheme which is called as Periodic Gate Optimization with QoS-awareness (PGO-QoS). This scheme is based on multi-threaded MPCP that has been previously proposed in the literature. In this paper, we briefly present PGO-QoS and discuss the impacts of buffer size on its performance under a feeder distance of 100 km. We show that PGO-QoS leads to shorter average delay than multi-threaded MPCP while slightly decreasing the packet loss ratio. As expected, larger buffer size increases the packet delay, especially under heavy loads. However, changing the buffer size does not affect the relative performance of PGO-QoS to the multi-threaded MPCP. Furthermore, we evaluate the effects of pre-specified delay bounds on the performance of PGO-QoS where each ONU runs an estimation to determine whether an incoming packet can reach the OLT within its delay bound.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".