Adaptive threshold-based scheduling for real-time and non-real-time traffic
Why this work is in the frame
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Bibliographic record
Abstract
A study on scheduling mixed real-time and nonreal-time traffic is presented. The work is motivated by the need to provide satisfactory performance tradeoffs in a dynamic load condition where the arrival rates and proportions of the real-time and nonreal-time packets vary with time. The authors first examine two threshold-based schemes, queue length threshold and minimum early threshold, and propose the corresponding adaptive schemes based on results from approximate analysis and simulation. The idea is to improve performance by adjusting tradeoff points adaptively as the arrival rates change. The authors further discuss the idea of integrating the two thresholds. The new algorithm ADP, is evaluated by simulation under various load conditions and compared with other common scheduling disciplines as well as an optimal offline algorithm. It is concluded that, by setting appropriate threshold functions in accordance with the requirements of applications, one can achieve satisfactory bounded loss ratio for real-time packets and acceptably low average delay for nonreal-time packets in a wide range of workload conditions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Full frame distilled prediction
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it