Input smoothing with buffering: a new technique for queueing in fast packet switching
Bibliographic record
Abstract
Because of the unscheduled nature of arrivals to a packet switch, two or more packets may arrive on different inputs destined for the same output. The switch architecture may allow one of these packets to pass through to the output, but others must be queued for later transmission. The performance of a new approach for providing queueing required to smooth fluctuations in packet arrivals to a high performance packet switch is studied. The new approach, called input smoothing with buffering, will allow the switch to accept a frame of b packets at each of its N inputs in b time slots. These packets are launched to a switch fabric of size Nb/spl times/Nb. If k packets destined to one output exceed the number b, k-b packets are stored in a queue assigned for this output inside the switch. This approach outperforms input smoothing and output buffering approaches. Performance issues such as cell loss and packet delay are discussed.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".