Factors affecting the efficiency of Demand-wise Shared Protection
Bibliographic record
Abstract
Demand-wise shared protection (DSP) can be thought of as a form of network-level 1:N APS protection scheme. The concept is to split total demand over multiple disjoint routes between each node pair and use one of the routes for protection. Recently referred to as demand-wise shared protection (DSP), this architecture is receiving renewed interest. Especially in high connectivity networks it would seem to offer the prospect of very low redundancy. In our results and others it is surprising, however, that the savings over 1+1 APS are small even when optimally designed. We have therefore tried to understand and explore the factors that affect the efficiency of DSP in terms of features of network topologies and demand patterns. Through this examination, factors affecting the efficiency of DSP are better understood, providing insights into the applicability of DSP as a protection architecture. One of the main insights is that while a k-way split over disjoint routes seems logically to promise ~1/(k-1) redundancy, this is largely overcome by the statistics of increased route length as k becomes greater than two.
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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 | 0.003 | 0.014 |
| 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 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".