Design of an adaptive PI rate controller for streaming media traffic based on gain and phase margins
Bibliographic record
Abstract
An adaptive proportional-integral (PI) rate controller for best-effort streaming media traffic in the Internet is proposed. Classical control theory is employed in the control design, which allows the user to achieve good performance of active queue management (AQM) in the router by specifying the proper gain and phase margins. The proposed adaptive PI rate controller will self-tune only when the number of active controlled source nodes changes or the average round trip time becomes longer. The adaptive PI rate controller located in the router can calculate the advertised source transmission rate for the streaming media traffic based on the instantaneous queue length of the buffer, which clamps the steady value of the queue length around the target buffer occupancy. Every controlled source node always transmits streaming media traffic through IP packets into the network at the maximum allowed transmission rate, thus providing the best-effort service traffic and maximising the bandwidth utilisation of the Internet. Our OPNET simulations demonstrate that the rate-based AQM control system can adapt to the fluctuation of the uncontrolled guaranteed traffic very well, thus providing the network with good stability robustness.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".