A new framework for rapid restoration in optical mesh networks
Bibliographic record
Abstract
A fundamental problem that needs to be addressed when designing survivable optical transport networks is the capability of these networks to quickly recover from element failures. Current restoration signaling is a two-phase messaging procedure between the sender and the receiver and depends heavily on the control message propagation delays during the recovery process and the optical cross-connect switching times. Offset time based restoration has been proposed to address [6] the impact of these network parameters and upper bound expressions have been derived. However, a closer investigation showed that as the network conditions change (e.g., increasing the number of wavelength channels per link) the benefits offered by the proposed framework would rapidly be depleted, and thereby rendering the argument of avoiding conventional restoration signaling inept. In this paper, we intend to re-use the same framework, however instead of using upper bound expressions to estimate the restoration times, we propose a more accurate model to estimate the offset time of each failed connection using a timedriven scheduling procedure. We show that propagation delays have very minimal impact on the network recovery times and switching delays are minimized whereas their accumulation along restoration routes is eliminated. We evaluate our proposal through simulation experiments and we show that by deploying a scheduling process, substantial restoration gain can be achieved under varying network conditions.
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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".