On survivable traffic grooming over logical topologies in WDM mesh networks
Bibliographic record
Abstract
Component failure in a WDM network is a serious problem that has attracted considerable attention in recent times. In a standard protection (or restoration) scheme the objective is to preserve the logical topology by switching over to back-up paths (or by setting up new lightpaths) after a fault occurs. In this paper we have proposed a new scheme where we handle a fault simply by modifying the traffic routing scheme to avoid the fault. We show that it is possible to guarantee that a significantly high number of requests for communication can be handled using this scheme, irrespective of the location of the fault. Two new integer linear program formulations have been presented using this approach. The first formulation assumes a fixed RWA, while the second finds the optimal RWA for maximizing guaranteed throughput. A large number simulation experiments demonstrate that, in the vast majority of cases, an optimal solution obtained using our second formulation not only generates a survivable routing but handles all the requests that the original fault-free logical topology was designed to handle.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".