Performance analysis of multimedia applications in Differentiated Services networks
Bibliographic record
Abstract
In this paper, we present simulation results assessing the performance of DiffServ-capable networks supporting real time multimedia traffic. We evaluate the performance of voice traffic, H263 video traffic and distributed interactive virtual environments (DIVE) applications with Web background traffic in a DiffServ-capable network. Particularly, the performance of weighted fair queuing (WFQ) algorithm is studied, and a realistic Web workload model generating aggregation of self-similar bursty traffic flows is developed and used to evaluate the performance of all above multimedia applications. Our analysis demonstrates that these multimedia applications can be well supported by DiffServ-capable networks even under the bursty Web style traffic, given that the subscribed bandwidth of the application is no less than its source generating data rate. According to our results, even for the most demanding DIVE application, bandwidth reservation at twice the source-generating rate is enough.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".