Performance analysis of the PetaWeb optical network architecture
Bibliographic record
Abstract
The dramatic increase of Internet users and the development of new high volume Internet applications has a profound impact on the design of next generation optical network (NGON) architectures. In order to cope with the immense diversity of applications and traffic volume, highly dynamic optical networks will be needed. Nortel Networks, proposed one kind of NGON architecture, known as PetaWeb. PetaWeb is based on a star topology, formed by an adaptive core and edge switches, which can accommodate traffic fluctuations by periodically reconfiguring the optical channels. This results in increased effective capacity of the network and reduction of the average delay and delay jitter. We implement a simulation model of the PetaWeb architecture and test its functionality under a variety of traffic loading conditions. The effect of traffic on the behavior of the protocols and the performance of the network is investigated by applying several traffic models. Simulation results are presented We have performed a thorough analysis how of performance related parameters are affected by the reconfiguration frequency, as well as the statistical behavior of the traffic.
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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 | 0.001 |
| 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".