Periodic GATE Optimization (PGO): A New Service Scheme for Long-Reach Passive Optical Networks
Bibliographic record
Abstract
In this paper, we present a new service scheme, Periodic GATE Optimization (PGO) for long-reach passive optical networks. PGO is based on a previously proposed service scheme where the optical line terminal (OLT) generates multiple threads to poll the bandwidth requests of the optical network units (ONUs). In PGO, OLT periodically builds an ILP formulation by using the collected requests of the ONUs, and solves the model for the overloaded ONUs. Based on the outputs, it determines how to credit the ONUs until the next ILP formulation whenever they are overloaded. We evaluate the performance of PGO by simulation, and show that it introduces further decrease in average packet delay until the heavy loads. Average queue length at the ONUs is also shortened by PGO. The results imply that PGO achieves these enhancements without increasing the packet drop probability. In the paper, we also present the quality-of-service (QoS) aware version of PGO, namely PGO-QoS. PGO-QoS consists of two modules such as intra-ONU scheduling where the dequeuing proportion of the QoS classes is determined and reported to OLT, and dynamic bandwidth allocation module where mostly PGO runs. We evaluate the performance of PGO-QoS for each QoS class and for the overall network in terms of average packet delay, average queue size, and packet drop probability under different scenarios. Based on the simulation results, PGO-QoS is shown to introduce further decrease in delay, queue length, and packet loss probability for the high priority class requests that are granted by multithread polling in Long-Reach PON.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".