Logical topology design for fault-tolerant WDM networks
Bibliographic record
Abstract
All-optical networks (AON), using wavelength division multiplexing (WDM), have become attractive candidates for building wide area networks (WANs). Finding a logical topology and a routing scheme making optimum use of network resources is a challenging task. A complication is that the survivability of AON has become an important issue. In designing a fault tolerant WDM network, the primary lightpaths, the corresponding backup lightpaths, and the routing scheme have to be determined simultaneously in such a way that network resources are used in an optimum manner. In this paper we first develop an Integer Linear Formulation (ILP) for designing a fault-tolerant logical using shared path protection. The objective is to design a survivable logical topology and a routing over that topology in such a way that the overall congestion is minimized. This formulation can be solved to give us the optimum logical topology for small WDM networks. However, for larger networks, this approach becomes infeasible due the large number of constraints and integer variables. For such networks, we outline a simple heuristic algorithm to find a feasible logical topology.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".