A New MPLS-based Local Failure Recovery for Multicast Communication
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Bibliographic record
Abstract
A new MPLS-based recovery approach for multicast trees is proposed. The main objective of the proposed approach is to trade off the extreme capacity consumed in the local recovery approach that builds a backup path for each element in the tree and the extreme time it takes to recover from the failure in the end-to-end recovery approach. Although the presented approach can be implemented in any large backbone network that employs multicast communication mode, we concentrate our discussion on the MPLS networks. After dividing the multicast tree into several domains, backup paths are set up between the border routers in each domain. In terms of the total reserved capacity, simulation results have shown that the performance of the proposed approach is close to the one produced by global recovery approach. In addition, the proposed approach outperforms the global recovery approach in terms of the average time needed to recover from link/node failure.
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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