Jitter Characterization in Admission Control and Pricing Issues in Integrated Multiservice Networks
Bibliographic record
Abstract
ABSTRACT: This paper analyses the pricing framework of multiservice networks and proposes an improved pricing scheme based on the effective bandwidth concept for taking into account quality of service parameters. Based on the deficiencies noted in the classical effective bandwidth scheme (intolerance to user uncertainty and no guarantee on jitter), we propose an improved charging function which gives more flexibility to the user and we introduce an additional constraint to take into account an eventual guarantee on the jitter or delay variation. We also extended the effective bandwidth pricing scheme to the case with guaranteed jitter, in order to take into account and better deal with the various QoS parameters to be considered in 3G networks. Our proposed charging function improves the classical effective bandwidth scheme, while remaining simple in that it requires that the network only monitors the average rate and duration of each connection. It is also fairer than the classical effective bandwidth scheme as it is more flexible related to user uncertainty and the incentive to an efficient use of network resource is preserved. The constraint on the guaranteed jitter was also tested and proved to be viable.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".