Connection management algorithm for advance lightpath reservation in WDM networks
Bibliographic record
Abstract
Advance reservation is a topic that is rarely discussed within the domain of wavelength division multiplexed (WDM) networks. However, for many emerging applications in the telecommunication and/or grid computing industries, a demand for a high bandwidth communication channel as well as a guarantee on resource availability certainly exists. Such applications include: remote surgery, remote experimentation with teleobservation capabilities, teleconferencing, and bulk transfers. In this paper, we present a new model for reserving advance lightpath requests in a centralized system. This model attempts to “migrate,” i.e., move previously reserved lightpaths to candidate wavelengths in order to lower the system’s blocking probability. We have tailored different lightpath migration algorithms to address two specific network objectives: 1) minimize the number of hops a new request traverses after migration, and 2) minimize the number of migrated lightpaths. In terms of blocking probability, the lightpath migration algorithms show a significant improvement over the original advance lightpath reservation model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".