Multiple-Link Failures Survivability in Optical Networks with Traffic Grooming Capability
Bibliographic record
Abstract
This paper investigates the problem of survivable traffic grooming (STG) in shared mesh optical networks and proposes different frameworks for improving the survivability of low speed demands against multiple near simultaneous failures. Capacity reprovisioning has recently been considered for improving the overall network restorability in the event of multiple failures by allocating protection resources after a failure to unprotected and vulnerable connections. In this paper we propose two different reprovisioning schemes (lightpath level reprovisioning, LLR, and connection level reprovisioning, CLR). Each of these schemes is suitable for a different survivable grooming policy. While LLR provides collective reprovisioning of connections at the lightpath level, CLR reprovisions spare bandwidth for lower speed connections instead. We study the performance of these schemes under two grooming policies (PAL and PAC), and we show that while CLR reprovisions substantially more connections than LLR, CLR yields a much better network robustness to near simultaneous failures due to its superior flexibility in using network resources.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".