Quality of service provisioning for VoIP applications with policy-enabled differentiated services
Bibliographic record
Abstract
The recent advancement in network technologies has made it possible for the convergence of voice and data networks. In particular, the differentiated services (DiffServ) model has helped in bridging the diverse performance requirements between voice and non real-time applications such as file transfer and e-mail. One key feature of the DiffServ model is its promise to bring scalable service discrimination to the Internet and intranets. With the heterogeneous nature of today's distributed systems, however, there is a demand for flexible management systems that can cope with not only changes in resource needs and the perceived quality of applications (which is often user-dependent and application-specific), but also changes in the configuration of the distributed environment. Policy-based management has been proposed as a means of providing dynamic change in the behavior of applications and systems at run-time rather than through reengineering. The paper proposes a policy-enabled DiffServ architecture to provide predictable and measurable quality of service (QoS) for voice over Internet Protocol (VoIP) applications, and illustrates its 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".