Proactive management of MPEG traffic in ATM networks using time sequenced RLS filters
Bibliographic record
Abstract
The capability of predicting VBR traffic can significantly improve the effectiveness of numerous management tasks such as dynamic bandwidth allocation and congestion control. This work demonstrates the applicability of time-sequenced adaptive filters in linear prediction of statistically multiplexed MPEG (Motion Pictures Experts Group) VBR (variable bit rate) video traffic. Time-sequenced adaptive filters allow for the cyclostationary nature of the input by periodically changing the filter and adaptation parameters. This predictor set up is ideal for predicting multiplexed MPEG traffic which has periodically recurring statistical properties and can be considered as a concatenation of PAR (periodic autoregressive) cyclostationary processes. The viability of the approach is illustrated through computer simulations. A number of half-hour long empirical MPEG-1 traces are multiplexed and, subsequently, the aggregated traffic is predicted. The RLS (recursive least squares) algorithm is used for adaptation. The results indicate that the RLS algorithm clearly outperforms the conventional LMS (least mean square) adaptive algorithm in terms of convergence speed and steady-state mean-square prediction error, and, hence, is a more suitable candidate for such an application.
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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 | 0.001 |
| 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".