Dynamic path-protected service provisioning in optical transport networks with a limited number of add/drop ports and transmitter tunability
Bibliographic record
Abstract
We consider path-based survivable service provisioning in transparent optical networks with the constraints of wavelength continuity and a limited number of add/drop ports at each OXC node in the presence of limited tunability of transmitters. We develop simple but valid analytical models to estimate the effects of number of add/drop ports and transmitter tunability on survivable service provisioning performance. We propose effective algorithms for the assignments of wavelength resources and add/drop ports for each survivable connection service and conduct simulations to evaluate the impacts of number of add/drop ports and transmitter tunability on path- based survivable service provisioning and further to examine the validity of the analytical models. It is found that a certain system add/drop ratio is required at each node so as to eliminate the blocking due to the lack of free add/drop ports. A network with a higher density requires a larger relative number of add/drop ports (i.e., add/drop ratio) for a given overall blocking objective. A network with a higher density benefits more in blocking from transmitter tunability. Finally, the analytical models are verified to be able to qualitatively predict the trends and effects of all the related constraints on the performance of survivable service provisioning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".