Traffic smoothing and bandwidth allocation for VBR MPEG-2 video connections in ATM network
Bibliographic record
Abstract
We studied the traffic smoothing and bandwidth allocation issues for VBR MPEG-2 video connections in an ATM network. First of all, the statistical characteristics of VBR MPEG-2 video sequences were examined by real observation. The obtained results showed that it would be very difficult to efficiently allocate an appropriate amount of bandwidth if such traffic were straightforwardly feed into the network. We therefore introduced a so called macro-frame (M-frame) smoothing scheme. It was found that the smoothed traffic not only possesses relatively simple characteristics but also makes the bandwidth allocation more efficient. In consideration of the fact that there is not yet a proper bandwidth allocation algorithm for VBR MPEG-2 connections, we examined and compared the simple peak rate allocation and Gaussian approximation. It was found that (1) peak rate allocation is far more inefficient than the actual need and (2) Gaussian approximation is surprisingly accurate and efficient in estimating the bandwidth, especially in the case of smoothed traffic.
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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".