Efficient Routing Using Flexible Ethernet in Multi-Layer Multi-Domain Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.002 |
| 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