Design of Adaptive PI Rate Controller for Best-Effort Traffic in the Internet Based on Phase Margin
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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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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 | 0.001 | 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 | 0.000 |
| 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)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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