Energy-Efficient Decentralized Framework for the Integration of Fog With Optical Access Networks
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".