A delay-based provisioning for Ethernet passive optical networks
Bibliographic record
Abstract
With the mass availability of Ethernet in local area networks (LANs) deployed globally and connecting LANs to metropolitan area networks (MANs) and wide area networks (WANs) via the current transport technologies there is a need to resolve bandwidth gap between access networks and transport networks. One of the proposals to overcome the famous last mile issue is to use a passive optical network (PON) for this networking segment. Varieties of access methods are under development to increase the offered potential transport capacity of passive optical networks (PONs). With the emergence of Ethernet as the convergence layer in access networks, a new breed of PON, namely Ethernet PON (EPON) has evolved. Ethernet passive optical network is one of the potential solutions, offering to solve the bottleneck issue of access networks and adaptability of carrying Ethernet frames from subscriber to service provider. In this study we will review Ethernet Passive Optical Networks (EPONs) and then we propose a delay-based provisioning algorithm to be able to offer differentiated services and QoS in EPON. Mathematical analysis and simulation results will be presented also.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".