Fuzzy leaky bucket congestion control in ATM networks with Markovian and self-similar traffic
Bibliographic record
Abstract
This paper discusses an ATM congestion control mechanism that introduces a leaky bucket control scheme based on fuzzy logic principles. Network congestion is described linguistically by introducing a fuzzy rule and appropriate fuzzy variables, and is treated mathematically via fuzzy set manipulations. With the application of fuzzy logic the complex mathematical treatment of classical feedback control is avoided, and the "hard" bound effect in the traditional LB is also eliminated in favor of "soft" bound membership functions. In order to evaluate the effectiveness of the fuzzy LB, the performance of the ATM network with a non-fuzzy adaptive LB mechanism is also investigated. Network parameters which affect the performance are identified and optimized for the two control schemes and a comparison of the two approaches is carried out under the same network condition and optimal parameters. Finally, the performance of the fuzzy LB and adaptive LB are also evaluated under self-similar traffic load. The performance analysis in this paper is based on simulation combined with numerical optimization method. Our results indicate that the fuzzy leaky bucket mechanism leads to significant improvement to the system performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".