A state-space reduction method for computing the cell loss probability in ATM networks
Bibliographic record
Abstract
Cell loss performance analysis in ATM networks has been considered as one of the most important issues in congestion control which is vital to the success of the ATM technique. In recent years, numerous approaches have been proposed for the computation of the cell loss probability in ATM networks. Among them are the stochastic fluid-flow (SFF) method and the Markov modulated deterministic process (MMDP) approach. The MMDP approach is basically a discrete version of the SFF method and it is numerical stable. Both approaches, however, require a considerable amount of computation time for problems of practical size. In this paper, we propose a state-space reduction method for both approaches. The idea is to find an appropriate tradeoff between the efficiency and accuracy. As a result, the proposed approach performs very well in terms of both accuracy and efficiency. For cases of practical interest (in which cell loss probabilities ranges from 10/sup -6/ to 10/sup -10/) its computation time is only a fraction of one CPU second.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.001 | 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".