Energy-Efficient Decentralized Framework for the Integration of Fog With Optical Access Networks
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 increasing numbers of broadband users and the corresponding rapid expansion of access networks have been putting more pressure on improving their energy efficiency to reduce both their operating costs and carbon footprint. At the center are passive optical networks (PONs), with them being one of the leading broadband technologies of today. Although they are considered to be the most energy-efficient among wired access technologies, their power consumption is still considerably high and is expected to increase over the next few years. As a result, many energy conservation frameworks have been proposed for PONs that are all centralized-based. In this paper, we propose a novel PON energy-conservation framework that is, for the first time, decentralized-based. Not only does the proposed framework aim to provide better network performance while conserving energy, but it also supports novel cloudlet placements for integrating fog computing with PONs by utilizing edge-to-edge communications. The framework is therefore designed to meet the requirements of next-generation access networks by addressing three main challenges; conserving energy, achieving high network performance, and supporting fog computing.
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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 | 0.001 | 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