Adaptive scheduling algorithms for Ethernet passive optical networks
Bibliographic record
Abstract
Medium access control (MAC) is one of the most crucial issues in the design of Ethernet passive optical networks (EPONs). To prevent data from collision in the upstream direction, an EPON system must employ a MAC mechanism to arbitrate the access to the shared upstream channel and at the same time efficiently share the bandwidth of the upstream channel among all optical network units (ONUs). In this paper, two adaptive scheduling algorithms for MAC in an EPON system are presented. One is called the longest-queue-first (LQF) algorithm, which adaptively schedules the transmission order of different ONUs based on the instantaneous queue length of each ONU and polls the one with the longest queue first in each polling. The other is called the earliest-packet-first (EPF) algorithm, which adaptively schedules the transmission order based on the arrival time of the first packet waiting in each ONU queue and polls the one with the earliest packet first. It is shown through simulation results that the proposed scheduling algorithms can effectively improve the network performance in terms of packet delay compared with the most commonly-used round-robin scheduling algorithm.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".