Optimized FlexEthernet for Inter-Domain Traffic Restoration
Bibliographic record
Abstract
Restoring traffic in multi-layer multi-domain networks (MLMD) can be inefficient and expensive due to the reconfiguration of both intra-domain and inter-domain paths under limited resources and information sharing. This often results in traffic loss and resource over-provisioning within the MLMD, leading to sub-optimal restoration throughput and high costs. In this study, we harness FlexEthernet (FlexE) on inter-domain links to maximize the restoration throughput at minimum cost. FlexE link aggregation is an effective technique to deal with the costly impact of alternative domain rerouting that allows diverting traffic over aggregated links parallel to the failed ones, without disrupting the intra-domain connections. Additionally, FlexE helps increase network reutilization by leveraging time division multiplexing (TDM) to flexibly shift affected traffic to underutilized aggregated links. However, scheduling traffic migration in FlexE is a challenging issue that has not been fully investigated in the literature. In this paper, we initially formulate the FlexE-based traffic restoration problem as a mixed integer non-linear program (MINLP) and then introduce an approximation algorithm to efficiently solve this problem in polynomial time. Furthermore, we propose a supervised learning approach to predict the optimal restoration policy for large-size instances. Experimental results show that our solution restores up to 14% more traffic than a state-of-the-art approach.
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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".