Integrating Fog With Long-Reach PONs From a Dynamic Bandwidth Allocation Perspective
Bibliographic record
Abstract
Integrating fog computing with optical access networks is believed to form a highly capable fronthaul that will live up to the various requirements and challenges of tomorrow's access networks. Such integration combines the high capacity of optical fiber with closer-to-the-edge computing and storage capabilities. However, because optical access networks were not originally designed to carry offloaded traffic nor support edge-to-edge communications, the network architecture and employed bandwidth allocation both need to be reconsidered in this new setting. In this paper, we study the offloading performance in a long-reach optical access network when the underlying bandwidth allocation is either centralized or decentralized. We investigate how offloading can be supported in each paradigm and develop an analytical framework that is tested against numerical results. The effect of offloading on regular upstream traffic is also examined as well as the effects of cloudlet placements and network extension on the offloading performance.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".