FPGA implementation of a modular and pipelined WF scheduler for high speed OC192 networks
Bibliographic record
Abstract
In this paper we propose an FPGA implementation of a multi protocol Weighted Fair (WF) queuing algorithm able to handle variable length packets targeted for Packet Over Sonet (POS) interfaces and ideal for the design of hybrid IP/ATM switches. Our contributions is an extension to an existing 4 channel scheduler architecture that combines the Highest Value First scheme and Round Robin scheme, to a modular multi channel scheduler design. The improvement we offer here compared to the previuous implementation is that we have used the existing 4 channel core module to build a higher order WF queuing system without decreasing its overall performance . As a result, our scheduler is general enough to accommodate ATM (UTOPIA Level3/4) , POS Phy Level3 (or PL3 for OC48) as well as POS Phy Level4 (or PL4 for OC192) interfaces.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".