Dynamic path restoration based on multi-initiation for GMPLS-based WDM networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a multi-initiation mechanism for dynamic path restoration to handle single-link failures in GMPLS-based WDM networks. This mechanism allows multiple network nodes on the primary path of a disrupted connection to participate in the restoration of the disrupted connection. Each of the nodes respectively initiates a restoration process upon the detection or notification of a link failure. In each of the processes, the initiating node attempts to dynamically establish a backup path for the disrupted connection. The destination node acts as a coordinator among multiple restoration processes. The purpose is to reduce the path restoration time so that a backup path can be provisioned more quickly for each disrupted connection that traverses a failed link. Based on this mechanism, a path restoration protocol is then presented and the performance of the protocol is evaluated through simulation experiments in terms of path restoration time and path restoration probability.
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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 | 0.000 |
| 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 it