Gaussian Process Regression Based Traffic Modeling and Prediction in High-Speed 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
Evolving nature of network traffic challenges existing models to fit and predict its behavior. In particular, real traffic modeling requires more flexible design that can adapt to long-range and short-range dependent traffic with dynamic patterns. Unfortunately, existing models cannot handle such requirements because various traffic behaviors such as periodic and self-similar are not taken into account. In this paper, Gaussian process regression (GPR) is adapted for traffic modeling and prediction. The connection between self-similarity as a traffic characteristic and GPR parameters has been driven and exerted to build of a new Hurst estimation method based on machine learning techniques. This led to propose self-similar covariance functions for enhancing prediction accuracy of GPR. The proposed GPR model has been applied for Hurst estimation as well as for traffic prediction on real traffic traces at different time-scales. The experimental results show the employment of self-similar covariance functions increases generalization ability of GPR for traffic modeling and prediction.
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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 | 0.001 |
| 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 it