Differentiated Quality-of-Recovery in Survivable Optical Mesh Networks Using $p$-Structures
Bibliographic record
Abstract
This paper investigates design methods of protection schemes in survivable WDM networks that use preconfigured protection structures ($p$-structures) in order to provide different quality-of-recovery (QoR) classes within 100% resilient single-link protection schemes. QoR differentiation is a practical and effective approach in order to strike different balances among protection cost, recovery delay, and management complexity. Based on the degree of pre-cross connectivity of the protection structures, we develop three design approaches of shared protection capacity schemes based on the following: 1) fully pre-cross-connected$p$-structures ($fp$-structures); 2) partially pre-cross-connected$p$-structures ($pp$-structures); and 3) dynamically reconfigured$p$-structures ($dp$-structures). In order to identify the optimal combinations of protection structures to meet the requirements of the three QoR classes, we use a column generation (CG) model that we solve using large-scale optimization techniques. Our CG decomposition approach is based on the separation processes of the design and selection of the protection structures. In the design process of the protection structures, the shape and protection capability of each$p$-structure is decided dynamically during the selection process depending on the network topology and the targeted QoR parameters. Extensive experiments are carried out on several data instances with different design constraints in order to measure the protection capacity cost and the recovery delay for the three QoR classes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".