Efficient Routing Using Flexible Ethernet in Multi-Layer Multi-Domain Networks
Bibliographic record
Abstract
Routing in multi-layer multi-domain (MLMD) networks is challenging due to different technologies and cooperation between different layers and domains. The MLMD routing problem has been considered in prior work, however most of them paid no attention to the inter-layer (or boundary) links, and the inter-domain routing is not yet optimized due to the lack of visibility over the intra-domain network topology. In this article, we investigate the problem of orchestrating MLMD networks by a hierarchical path computation engine (PCE) to leverage the performance of FlexE-the new Flexible Ethernet technology used to link IP and optical domains. We model the routing and FlexE assignment problem for FlexE-Aware and FlexE-Unaware modes and derive an efficient algorithm that optimizes the network utilization by a hierarchical allocation of bandwidth. Facing the issue of missing network information in an MLMD network due to privacy and security reasons, our orchestrator uses a new implicit routing strategy for gathering intra-domain information where the boundary link metrics are considered. Experimental results show that the proposed solution achieves 87% of the optimal throughput, a performance significantly higher than the current practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".