QoS performance in IP over PetaWeb optical network
Bibliographic record
Abstract
The dramatic growth of the Internet traffic and corresponding specific IP service requirements motivate the Internet service providers, network equipment manufacturers, and researchers not only to focus on scalability and cost effectiveness of the next generation optical network architecture design, but also on the need to provide assured quality of service (QoS) for Internet applications. We address the issues of supporting the QoS in one alternative optical network called PetaWeb network, proposed by Nortel Networks. PetaWeb is based on the use of adaptive core and edge switches, which can accommodate traffic fluctuations through reconfiguration of channels periodically. In order to achieve end-to-end QoS over PetaWeb network, a QoS-aware edge node and QoS-aware channel allocation algorithm come in consideration. This increases the effective capacity of the network, reduces packet loss and packet delay, so satisfying the desired services. Simulations are also implemented to evaluate and verify the network QoS performance.
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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 | 0.000 |
| 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".