Delay-Constrained Admission and Bandwidth Allocation for Long-Reach EPON
Bibliographic record
Abstract
Next generation Passive Optical Network (PON)technology has been evolving to consolidate the metro andaccess networks in order to offer enhanced capacity, highsplit ratio and reduced deployment cost per subscriber.However, transmission of the signals to long distances up to100km leads to increased propagation delay whereas highsplit ratio may lead to long cycle times resulting in largequeue occupancies and long packet delays. In this article, wepresent a delay-constrained admission control mechanismand adapt this scheme to our previously proposed bandwidthallocation technique, namely Periodic GATE Optimization(PGO). We call this new scheme Delay-Constrained PeriodicGATE Optimization (DC-PGO). DC-PGO is designed to runfor multiple service classes as it inherits the advantages ofPGO by periodically building and solving an ILP formulationat the OLT in order to obtain the appropriate creditvalues for the overloaded ONUs. At the ONU side, DCPGOruns an admission control scheme before pushing thearriving packets in the sub-queues. The admission controlscheme uses statistical information consisting of the localdata at the ONU and the previously received GATE messagesfrom the OLT. Through simulations, we show that DC-PGOenhances the performance of multi-threaded polling in longreachEthernet PON when packets of differentiated serviceclasses arrive with pre-specified delay requirements.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".