An exact approach for finding bicriteria maximally SRLG-disjoint/shortest path pairs in telecommunication networks
Bibliographic record
Abstract
The paper addresses a bicriteria optimisation problem in telecommunication networks that aims at finding Pareto efficient pairs of paths between two given nodes, seeking to minimise the number of SRLGs (Shared Risk Link Groups) common to both paths and the path pair cost. This problem is of particular importance in telecommunication routing design, namely concerning resilient routing models where both a primary and a backup paths have to be calculated to minimise the risk of failure of a connection between origin and terminal nodes, in case of failure in the primary path. An exact resolution method is applied for solving this problem, enabling the calculation of the whole set of Pareto optimal solutions, which combines a transformation of the network representation with a path ranking algorithm. A comprehensive experimental study on the application of this approach, using reference network topologies, considering random SRLG assignments to the links and random link bandwidth occupations, together with the discussion on typical examples of solution selection and potential advantages of the method, are presented.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".