Towards More Dynamic Energy-Efficient Bandwidth Allocation in EPONs
Bibliographic record
Abstract
Power conservation in passive optical networks (PONs) has been an active area of research since the cyclic sleep-mode was first proposed for optical network units (ONUs). Many studies have then settled upon locking downstream and upstream transmissions for each ONU in a cyclic fixed slot, thus allowing the ONU to switch to sleep-mode for the rest of the transmission cycle. However, such fixed allocation limits the flexibility and dynamicity of the bandwidth allocation and leads to upstream underutilization. Moreover, to maximize power conservation, the cycle duration used must be long enough to make up for mode-switching overheads, which significantly degrades the network performance in terms of packet delays. In this paper, we develop a novel energy-efficient framework for Ethernet PONs (EPONs). To that end, we propose different upstream allocation schemes to improve the fixed-slot performance while maintaining energy-efficiency at acceptable levels. We also propose a more accurate arrangement for downstream-upstream locking. Moreover, we use a long-reach PON setting, where the long propagation delays impact the network performance posing further challenges to the bandwidth allocation. Numerical results show that, under heavily loaded network conditions, packet delays can be reduced by around 60% at an additional power cost of less than 5%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".