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.>
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".