A Distributed Dynamic Bandwidth Allocation Algorithm in EPON
Bibliographic record
Abstract
EPON (Ethernet Passive Optical Network) is a rising bandwidth access technology, and it could realize the comprehensive operation access including data, video, and voice, with good economic characters. IEEE 802.3ah is the industrial standard of EPON, but it doesn’t concretely regulate the uplink bandwidth allocation algorithm of EPON. Therefore, aiming at the uplink channel access of EPON, people have put forward various dynamic bandwidth allocation algorithms, but most of them belong to intensive algorithm, i.e. the distributed bandwidth allocation (DBA) algorithm runs in OLT which is the interceder to allocate the uplink transmission time slot for ONU. A new distributed dynamic bandwidth allocation algorithm (DDBA) is proposed in this article, in which ONU decides the size of transmission window based on the assistant information transmitted by OLT and self queue length. The simulation result indicates that comparing with IPACT (Interleaved Poling with Adaptive Cycle Time) (G. Kramer, 2002, P.89-107), under the high network load, DDBA could obviously improve the average end-to-end time delay and the average queue length.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".