Physical-Layer p-Cycles Adapted for Router-Level Node Protection: A Multi-Layer Design and Operation Strategy
Bibliographic record
Abstract
We study an idea for efficient integrated protection of an IP over WDM network using the spare capacity of span-protecting p-cycles to provide protection against both optical link failures and router node failures in the IP/MPLS layer. Previously, networks using span-protecting p-cycles were designed from the point of view of a single-layer physical transport network. Also separately, it has been known how to plan capacity allocations to IP-layer logical links to support controlled levels of bandwidth oversubscription on transiting traffic flows through a failed router or ATM node. To address both failure types, however, it is unattractive to simply combine the two investments in added protection capacity. Accordingly, we have studied ways to plan a 100% span-restorable set of physical-layer p-cycles so that very high levels of transiting traffic flow can also be restored upon MPLS router node failure, with controlled worst-case oversubscription of LSP capacity allocations. In test cases it was found that the spare capacity provided by span-protecting p-cycles can provide a high level of node protection depending on the strategy used for exploring the p-cycle capacity. We think this leads to novel and efficient strategies for multi-service and multi-layer survivable traffic engineering and bandwidth management
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".