Frame Level Sharing for DBA virtualization in multi-tenant PONs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 it