Design of Adaptive PI Rate Controller for Best-Effort Traffic in the Internet Based on Phase Margin
Bibliographic record
Abstract
In this paper, we propose an adaptive PI (proportional-integral) rate controller for the AQM (active queue management) router that would support best-effort traffic in the Internet. Unlike most window-based controllers, our rate-based controller design is derived from the classical control theory and it would allow the users to achieve good stability robustness of the AQM control system by specifying a proper phase margin. We also make our controller adaptive by selecting a simple heuristic parameter to monitor the network environment real-time so that the controller would self-tune only when a dramatic change of the network traffic has drifted the monitoring parameter outside its specified interval. Located in the router, the adaptive PI rate controller calculates desirable source window sizes (i.e., source sending rates) based on the instantaneous queue length of the buffer and advertises it to the sources. Our simulations demonstrate that our AQM control system can adapt very well to sudden changes in network environment, thus providing the network with good transient behavior. By making the source sending rate relatively smooth, our adaptive PI rate controller becomes quite suitable for streaming media traffic control in the Internet
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".