OPN08-03: Backup Path Re-optimizations for Shared Path Protection in Multi-domain Networks
Bibliographic record
Abstract
Within the context of dynamic routing models for shared path protection in multi-domain networks, we propose a backup path re-optimization phase with possible rerouting of the existing backup paths in order to increase the bandwidth sharing among them while minimizing the network backup cost. The re- optimization phase is activated periodically or when routing a new connection fails because of insufficient capacity. Three re- optimization models are discussed: i) Global rerouting where the re-optimization is performed once for the entire network; ii) Local rerouting where the re-optimization is serially performed on one domain at a time or on selected domains, and iii) Local rerouting with least effort, i.e., where the smallest possible number of backup path reroutings is performed in order to be able to handle new connection requests. The first model offers the best resource savings while the two others are more scalable in multi-domain networks. Comparative performance of the three models are conducted and numerical results 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 | 0.000 | 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 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".