The output-controlled round robin scheduling in differentiated services edge switches
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a new scheduling technique, called output-controlled round robin (OCRR), for the support of class-based IP networks with variable packet sizes. This technique can be used in edge switches of an optical network to assemble traffic from aggregated flows into bursts/slots. Traffic flows from different classes are scheduled in logical frames. In each logical frame, a flow obtains a grant quantum with respect to its average rate and bandwidth usage in order to pass its traffic. The round robin technique is extended to include smaller cycles to send packets from aggregate flows one by one, thus, eliminating bursty effect from the same flow on the output bandwidth. The amount of the output traffic from each flow is also controlled. Our performance evaluation demonstrated that this output-controlled technique has better performance results than the deficit round robin algorithm in reduction of queuing delay and burst size from the same flow.
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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 | 0.000 | 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)
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