Internet performance modeling using mixture dynamical system models
Bibliographic record
Abstract
This paper models Internet traffic input stream and TCP connection durations using dynamical system models. A linear dynamical model with mixture Gaussian output is proposed for the Internet traffic input stream, and a linear dynamical system with mixture lognormal output is developed to model the TCP connection durations. In the proposed models, a sum of independent AR (Anderson and Nielsen, 1998) processes is used to approximate the autocorrelation of the real data, and a Gaussian mixture or lognormal mixture is used to fit the marginal distribution. As a result, the output processes can capture the correlation and the marginal distribution simultaneously. Making use of the fact that at each iteration the parameter increment of the EM algorithm has a positive projection on the gradient of the likelihood, a stochastic approximation-based recursive EM algorithm is proposed to fit the traffic marginal distribution, A cross-validation criterion is used for the model selection. To illustrate the usefulness of the proposed models, several experimental results are provided.
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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 | 0.002 |
| 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".