Bibliographic record
Abstract
Integrating optical access networks with fog computing combines the high capacity of optical fiber with closer-to-the-edge computing and storage capabilities. Such integration is believed to form a highly capable backhaul that will alleviate network congestions, serve local demands with less energy consumption, and live up to the requirements of tomorrow's access networks and application requirements. This integration however requires reexamining the bandwidth allocation in addition to reconsidering the network architecture itself, which was not designed to support direct edge-to-edge communications. Moreover, the growing demands for energy-efficient access networks also add the requirement for sleep-aware bandwidth allocation and a power-conserving framework. In this paper, we study the offloading performance in a long-reach passive optical network (LR-PON) when the underlying bandwidth allocation is either centralized or decentralized. Moreover, we investigate how offloading can be supported in each paradigm when optical network units (ONUs) go through a cyclic sleep-mode to conserve energy. We consider this paper to be one of the first to look into integrating fog with LR-PONs under a power-conserving framework and examine which allocation paradigm would be fit to carry offloaded traffic with better network performance and energy-efficiency within this new setting.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".