An analytical approach to the performance evaluation of the balanced gamma switch under multicast traffic
Bibliographic record
Abstract
Abstract This paper presents the performance evaluation of a new cell‐based multicast switch for broadband communications. Using distributed control and a modular design, the balanced gamma (BG) switch features high performance for unicast, multicast and combined traffic under both random and bursty conditions. Although it has buffers on input and output ports, the multicast BG switch follows predominantly an output‐buffered architecture. The performance is evaluated under uniform and non‐uniform traffic conditions in terms of cell loss ratio and cell delay. An analytical model is presented to analyse the performance of the multicast BG switch under multicast random traffic and used to verify simulation results. The delay performance under multicast bursty traffic is compared with those from an ideal pure output‐buffered multicast switch to demonstrate how close its performance is to that of the ideal but impractical switch. Performance comparisons with other published switches are also studied through simulation for non‐uniform and bursty traffic. It is shown that the multicast BG switch achieves a performance close to that of the ideal switch while keeping hardware complexity reasonable. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".