Fast simulation for self-similar traffic in ATM networks
Bibliographic record
Abstract
Self-similar (or fractal) stochastic processes were proposed as more accurate models of certain categories of traffic (e.g., Ethernet traffic, variable-bit-rate video) which will be transported in ATM networks. Existing analytical results for the tail distribution of the waiting time in a single server queue based on fractional Gaussian noise and large deviation theory, are valid under a steady-state regime and for an asymptotically large buffer size. However, the predicted performance based on steady-state regimes may be overly pessimistic for practical applications. Theoretical approaches used to obtain the transient queueing behavior and queueing distributions for a small buffer size become quickly intractable. The approach we followed was based on fast simulation techniques for the study of certain rare events such as cell losses with very small probability of occurrence. Our simulation experiments provide an insight on the transient behavior that is not possible to predict using current analytical results. Finally they show good agreement with existing results when approaching steady-state.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".