Similarities between voice and high speed Internet traffic provisioning
Bibliographic record
Abstract
The paper finds similarities between voice traffic and high speed Internet data traffic characteristics from a facility provisioning perspective. Telephone switch traffic measurements are used to show that self-similarity is present in a voice traffic time series, to identify the factor associated with self-similarity, and then to demonstrate that traditional voice traffic provisioning methods remove self-similarity. Voice traffic methods and models are then applied with some modifications to a high speed Internet traffic series for various subscriber aggregations and time scale resolutions. Voice traffic models are found to be applicable to data traffic when it is processed in a similar way to that for voice traffic. The conclusions are based on model fitting results and goodness-of-fit tests for weekday busy hour Internet data traffic loads. The similarities appear to be strong enough that telephone company operations support systems and provisioning methods may require only relatively small modifications and extensions to support both voice and high speed Internet services. The findings can also benefit cable companies offering voice and data services.
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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 | 0.000 |
| 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 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".