Fast simulation for self-similar traffic in ATM networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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