Performance analysis of optical burst switching networks with and without class isolation
Bibliographic record
Abstract
This paper presents an analytical model that evaluates the blocking probability of each service class in optical burst-switching networks. The model is applicable to systems with arbitrary burst length distributions and arbitrary-sized QoS header offsets. Thus, unlike previous models, it is applicable to the design and study of networks with a wide range of traffic characteristics, including systems in which higher classes are not necessarily isolated from lower classes and systems in which the conservation law does not necessarily hold. We derive explicit expressions for blocking probability both the cases of constant burst lengths and exponentially distributed burst lengths and verify the model's accuracy through simulation. We show the model to be accurate for a number of different traffic loads and class priorities. For an OBS system with two classes and a 1:10 ratio of high-priority to low-priority traffic, our model is able to predict accurately the blocking probability for each class, whereas the predictions from a model that assumes isolation deviates by as much as an order of magnitude from the simulation results for the higher priority class.
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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".