TBLB algorithm for servicing real-time multimedia traffic streams
Bibliographic record
Abstract
In this paper, we propose to use a simple packet servicing algorithm suitable for servicing bursty real-time multimedia traffic streams in packet-switched networks. These real-time multimedia services may include packetized voice and videoconference/playback. The servicing mechanism is an enhancement of the token bank leaky bucket (TBLB) scheme we proposed previously. This new algorithm combines both the servicing and the policing functions, and its performance in accommodating bursty real-time traffic is evaluated by computer simulations. We show that the quality of service (QoS) performance (mean delay and jitter) of TBLB exceeds that of the leaky-bucket constrained generalized processor sharing (GPS). Although GPS has been proven to give bounded delay to a leaky-bucket constrained traffic stream and ensure instantaneous fair allocation of bandwidth, the average delay is often quite large. Also, fairness is not a guarantee of QoS, and is not perceived by users directly. Another property that is often neglected in the analysis of schedulers (but very important to user QoS) is the sensitivity of QoS to deviations of traffic streams from their specified traffic descriptors. We present results to show that our proposed method is relatively robust to such deviations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".