α_ SNFAQM: an active queue management mechanism using neurofuzzy prediction
Bibliographic record
Abstract
Active Queue Management (AQM) policies are mechanisms for congestion avoidance, which pro-actively drop packets in order to provide an early congestion notification to the sources. Random Early Detection (RED), the defacto standard and its different flavors have been proposed as simple solutions to the AQM problem. However, these approaches require manual tuning and fail to accurately capture variations in the input traffic, thereby resulting in unstable behavior. α_SNFAQM is a new AQM mechanism that uses a neurofuzzy prediction method (α_SNF) to capture traffic variation and accurately detect the future congestion. It distinguishes (i) severe congestion and (ii) light congestion. We compare the performance of α_SNFAQM with other AQM schemes like RED, PAQM and APACE in a bottleneck link. Simulation results have shown that α_SNFAQM outperforms other AQM schemes in stabilizing the instantaneous queue length, reducing packet loss ratio while keeping a high utilization of the link.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".