Intelligent Active Queue Management Using Explicit Congestion\n Notification
Bibliographic record
Abstract
As more end devices are getting connected, the Internet will become more\ncongested. Various congestion control techniques have been developed either on\ntransport or network layers. Active Queue Management (AQM) is a paradigm that\naims to mitigate the congestion on the network layer through active buffer\ncontrol to avoid overflow. However, finding the right parameters for an AQM\nscheme is challenging, due to the complexity and dynamics of the networks. On\nthe other hand, the Explicit Congestion Notification (ECN) mechanism is a\nsolution that makes visible incipient congestion on the network layer to the\ntransport layer. In this work, we propose to exploit the ECN information to\nimprove AQM algorithms by applying Machine Learning techniques. Our intelligent\nmethod uses an artificial neural network to predict congestion and an AQM\nparameter tuner based on reinforcement learning. The evaluation results show\nthat our solution can enhance the performance of deployed AQM, using the\nexisting TCP congestion control mechanisms.\n
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".