Reduced load approximations for large-scale optical WDM networks offering multiclass services
Bibliographic record
Abstract
We propose an approximation method for performance analysis of large-scale optical WDM networks (comprising multiple interconnected optical WDM networks) that offer multiclass services. In such networks, lightpaths are dynamically set up and terminated between source and destination nodes in order to serve calls in various service classes, and these lightpaths traverse through a number of interconnected optical WDM networks that may be in different administrative domains. Each service class is characterized by its resource requirements (number of wavelengths needed for a call) and expected call holding time (or subscription period). We model the large-scale optical network as a two-level hierarchical multiclass loss network; the lower level of hierarchy consists of individual optical WDM networks (network segments). Each network segment is abstracted to a logical node at the higher level of hierarchy, and these logical nodes are connected through logical links. Then, we present a methodology of approximately computing per class end-to-end approximate blocking probabilities.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".