Design of a meta-mesh of chain subnetworks: enhancing the attractiveness of mesh-restorable WDM networking on low connectivity graphs
Bibliographic record
Abstract
We have developed a design refinement to increase the capacity efficiency of span-restorable mesh networks on sparse facility graphs. The new approach views the network as a "meta-mesh of chain subnetworks". This makes the prospect of WDM mesh networking more economically viable than with previous mesh-based designs where the average nodal degree is low. The meta-mesh graph is a homeomorphism of the complete network in which edges are either direct spans or chains of degree-2 nodes. The main advantage is that loop-back-type spare capacity is provided only for the working demands that originate or terminate in a chain and not for the entire flow that crosses a chain. The transiting ("express") flows are entirely mesh-protected within the meta-mesh graph which is of higher average degree and hence efficiency for mesh restoration than the network as a whole. Nodal equipment savings also arise from the grooming of express lightpaths onto the logical chain-bypass span. Only the meta-mesh nodes need optical cross-connect functionality. Other sites use OADMs and/or glassthroughs. The resultant designs comprise a special class of restorable network that is intermediate between pure span restoration and path restoration. Most of the efficiency of path restoration is achieved, but with a span restoration mechanism which is more localized and potentially faster and simpler than path restoration. The concept lends itself to implementation with OADMs having a passive waveband pass-through feature to support the logical chain bypass spans for express lightpaths.
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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 | 0.000 |
| 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".