Availability analysis of span-restorable mesh networks
Bibliographic record
Abstract
The most common aim in designing a survivable network is to achieve restorability against all single span failures, with a minimal investment in spare capacity. This leaves dual-failure situations as the main factor to consider in quantifying how the availability of services benefit from the investment in restorability. We approach the question in part with a theoretical framework and in part with a series of computational routing trials. The computational part of the analysis includes all details of graph topology, capacity distribution, and the details of the restoration process, effects that were generally subject to significant approximations in prior work. The main finding is that a span-restorable mesh network can be extremely robust under dual-failure events against which they are not specifically designed. In a modular-capacity environment, an adaptive restoration process was found to restore as much as 95% of failed capacity on average over all dual-failure scenarios, even though the network was designed with minimal spare capacity to assure only single-failure restorability. The results also imply that for a priority service class, mesh networks could provide even higher availability than dedicated 1+1 APS. This is because there are almost no dual-failure scenarios for which some partial restoration level is not possible, whereas with 1+1 APS (or rings) there are an assured number of dual-failure scenarios for which the path restorability is zero. Results suggest conservatively that 20% or more of the paths in a mesh network could enjoy this ultra-high availability service by assigning fractional recovery capacity preferentially to those paths upon a dual failure scenario.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".