Input-output-buffered ATM switches with delayed backpressure mechanisms
Bibliographic record
Abstract
This paper presents a study of the effect of the delay in the back-pressure signal in an architecture with input-output-buffering with back-pressure control, on the switch performance. The exact value of the delay depends on the specific implementation of the back-pressure mechanism and the contention resolution policy. This involves the mechanism by which the information is broadcasted to the input ports. The study demonstrates that the delay in the back-pressure signal seriously affects the switch performance, since it could result in severe cell loss at the output ports. This cell loss at the output ports can be controlled by modifying the output queue management mechanism, i.e. by incorporating additional buffering at the output ports on top of the original output queue size. The amount of this additional buffering depends on the value of the delay in the back-pressure signal, and it does not seriously affect the size of the input buffers. Higher values of delay in the back-pressure signal do not adversely affect the cell loss at the input ports. This study also investigates the overall input and output buffer allocation policies to achieve acceptable cell loss performance for architectures with delayed back-pressure mechanisms.>
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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 | 0.002 |
| 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 | 0.000 | 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".