Dynamic protection provisioning with FIPP p-cycles in WDM networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Application opportunities associated with video, voice and data triple play result in a dramatic demand increase in metro transport networks, with many traffic patterns becoming increasingly dynamic and difficult to predict. This is driving the need of core networks with a high degree of flexibility and granularity to carry traffic. We propose to investigate the question of what this means in terms of dynamic protection provisioning. In other words, we want to study how much a dynamic traffic affects the protection structures and how stable are the protection structures under dynamic traffic. While most studies on the stability of protection structures have been done on p-cycles and link shared protection, we investigate here the stability of FIPP p-cycles under dynamic traffic. For doing so, we design and develop a scalable mathematical model that we solve using large scale optimization tools. Numerical results show that FIPP p-cycles are remarkably stable with respect to the evaluation of the number of cross-connections to be reset under dynamic traffic.
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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 | 0.001 |
| 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 it