Performance behavior evaluation of Internet congestion control policing mechanisms
Bibliographic record
Abstract
Performance behavior is an important issue in the design and implementation of an efficient Internet congestion control policing mechanism. The effectiveness of such a mechanism can be measured by packet loss probability, bandwidth allocation, packet delay, throughput or other quality of service measures. In this paper, we carry out a comprehensive study to investigate the performance behavior of four selected policing mechanisms for the Internet namely: token bucket (TB), jumping window (JW), triggered jumping window (TJW) and exponentially weighted moving average (EWMA). Three types of bursty sources modeled as On/Off, Poisson and batched Poisson processes are utilized. Three criteria are used to evaluate the performance behavior of the selected policing mechanisms. These are the average packet delay, the average packet loss probability and the average number of lost credits. Computer simulations were used to arrive at various conclusions regarding the dependence of performance on source traffic characteristics and policing mechanism parameters. Furthermore, a comparison of the performance behavior of the selected policing mechanisms was carried for different input traffic characteristics.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".