Flexible Ethernet Traffic Restoration in Multi-layer Multi-domain Networks
Bibliographic record
Abstract
Recently, Flexible Ethernet (FlexE) has emerged as a new transmission technology allowing the flexible utilization of optical transport. This flexibility helps improve network ability against failures. FlexE recovers from a physical link (PHY) failure by migrating traffic to a new PHY. However, this task is costly, especially for critical failures or when the network is under high utilization. In this paper, we investigate the FlexE Traffic Restoration (FTR) problem that aims to maintain high network utilization by the fast recovery of FlexE clients with the minimum cost using the spare capacity in the already deployed PHYs. High network utilization can be obtained by rerouting of FlexE subgroups, moving clients to another subgroup, and shifting the clients’ slots in the same subgroup without traffic disruption. We formulate the FTR optimization problem and solve it in polynomial time using learning theory and approximation. Experiments carried out in a real testbed show the proposed solution recovers 63% more traffic than baseline restoration schemes.
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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 | 0.001 |
| 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".