Optimal and Efficient Design of Ring Instances in Metro Ethernet Networks
Bibliographic record
Abstract
Ethernet Ring Protection (ERP) switching has emerged to provide sub-50 ms of restoration times, allowing Ethernet technologies to expand beyond enterprises to next generation metro and backbone networks, providing much needed services to interconnect for instance dispersed and high-bandwidth data centers. This paper considers the problem of efficiently designing and planning an Ethernet-based metro network with ERP protection method. While previous recent work has addressed such design problem, none has considered the capabilities of exploiting multiple ERP instances, leaving behind some advantages that network providers could tap into to provide their customers with desirable quality of service support. Resource planning in ERP-based Ethernet network is, however, a complex problem due to the challenges associated with the logical link block selection as well as ring hierarchy selection. ERP instances add, however, another dimension of combinatorial complexity, making the design problem completely intractable. To address this issue, we resort to large scale optimization tools and present a novel primal-dual decomposition of the original problem using column generation. We show that our method is very scalable and obtain several design insights on various representative network instances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".