Frame Level Sharing for DBA virtualization in multi-tenant PONs
Bibliographic record
Abstract
The worldwide installation of Fiber-to-the-premises (FTTP) access network solutions is hindered by the high upfront cost of deploying ubiquitous fiber infrastructure. While passive optical networks can provide lower cost compared to point-to-point solutions, their total cost of ownership is still high for most operators to justify a mass scale deployment. Sharing passive optical network (PON) infrastructure has thus been proposed as a solution for network operators to reduce the cost of running FTTP services. In addition, the ability for operators to offer business services (including for example mobile backhaul) in addition to residential services, is crucial to increase the overall PON network revenue. However running services with highly diverse requirements over a physical infrastructure shared among multiple operators (which we now refer to as virtual network operators -VNOs) requires VNOs to have a tight control over PON capacity scheduling. In this paper, we introduce a novel upstream PON capacity sharing algorithm called Frame Level Sharing (FLS). FLS is based on the idea of virtual Dynamic Bandwidth Assignment (vDBA), and allows sharing the upstream frame among multiple VNOs to maximize bandwidth utilization, minimize latency, and provide a high level of service isolation among the VNOs sharing the PON. Our simulation results show that FLS outperforms other benchmark algorithms proposed in the literature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".