Self-Tuning Utility-Based Controller for End-to-End Congestion in the Internet
Bibliographic record
Abstract
In this paper, we design a self-tuning utility- based controller for end-to-end congestion in the IP (Internet protocol)-based Internet. Multiple controlled sources transmit the packets through a series of AQM (Active Queue Management) routers into their destinations simultaneously and share the limited bandwidth of the Internet. Each AQM router runs the RED (Random Early Detection) algorithm that uses ECN (Explicit Congestion Notification) packet marking strategy to provide the link congestion information through IP packets. A self-tuning utility-based controller is placed in every source node to regulate source transmission rate based on the feedback route congestion information from the AQM routers through ACK packets. The pole placement technique in classical control theory is used to allow the user to achieve good transient network performance. By assigning a proper interval of damping ratio ζ, ach controller self-tunes only when the change of network parameters drifts ζ outside its specified interval. Our simulations demonstrate the stability of the Internet achieved by our self-tuning utility-based congestion controller.
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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 | 0.001 |
| 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) | 0.001 | 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".