Performance Evaluation of an Optical Burst Switched Core Node With Generalized Arrivals and Partial Wavelength Conversion Capability
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Bibliographic record
Abstract
Many of the burst assembly algorithms employed in optical burst switching (OBS) networks preserve the IP traffic self-similarity property in the burst traffic. We introduce a mathematical model for performance evaluation of an OBS core node employing either no, a partial or a full wavelength conversion strategy. The model assumes long-range dependent (LRD) traffic arrivals to the OBS intermediate node whose inter-arrival times are accurately modeled by a Pareto distribution, whereas exponential holding times are assumed. In our proposed model, each output port in the node is modeled as a GI/M/w/w queue with partial server accessibility. An imbedded Markov chain approach is used to derive the limiting state probability distribution for the number of bursts currently served by an output port as seen by arriving bursts. Next, the average burst loss probability is evaluated from steady-state occupancy probabilities. In addition, the results of our mathematical model are validated via simulation. Furthermore, the results of the model are compared with those when assuming short-range dependent Poisson arrivals. Comparison shows that traditional Poisson traffic models yield over-optimistic performance measures compared to the LRD Pareto traffic models, especially for light traffic scenarios. Furthermore, we study the impact of varying different traffic parameters, such as the average arrival rate and the Hurst parameter, on the burst loss probability. Finally, the impact of varying the wavelength conversion capability on the burst loss probability is studied, where we compare two strategies for contention resolution: adding new channels (wavelengths) or adding wavelength converters, while taking the cost into consideration.
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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 | 0.001 | 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 it