Toward Parallel Edge Computing in Long-Reach PONs
Why this work is in the frame
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Bibliographic record
Abstract
The huge bandwidth capacities and low costs of passive optical networks (PONs) combined with their high data rates have made them strong candidates for wireless backhauls. Many designs have therefore been proposed to integrate PONs with edge and fog computing paradigms, which are essential for many emerging applications. However, the feasibility of this integration has not yet been fully examined. The dynamic bandwidth allocation (DBA) that would best support this integration and the effect it would have on network performance have not yet been studied. In this paper, we study the performance of edge computing in long-reach PONs (LR-PONs), where long propagation delays pose challenges to the bandwidth allocation performance. We believe this paper is one of the first to study the feasibility of edge computing in these optical access networks by investigating whether centralized or decentralized allocation would be better to support computational offloading to the edge. We compare centralized multithread polling and a modified decentralized scheme in terms of offloading delays, effects on upstream traffic delays, and throughput.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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