Fast Efficient Design of Shared Backup Path Protected Networks Using a Multi-Flow Optimization Model
Bibliographic record
Abstract
Core communication networks have seen significant traffic increases in recent years, and availability requirements also continue to increase. This fact has led to a wide array of network design improvements, particularly in the area of network survivability. The various survivability mechanisms and accompanying design models that have been developed use diverse strategies to provision spare capacity throughout a network to restore traffic in case of a failure. The break of a fiber line continues to be the most common type of network failure, and this paper addresses at a common protection mechanism called shared backup path protection (SBPP), which is quite efficient at dealing with this type of failure. SBPP is a popular survivability mechanism, and there has been a significant amount of work done with it in recent years. However, the SBPP integer linear program (ILP) design model has proven difficult to solve using reasonable computing and time resources. While many algorithms and heuristics have been developed to design SBPP-based networks, it has been difficult to know how well these designs perform compared to ILP optimized networks. This paper presents a new SBPP-type protection mechanism and accompanying ILP model that solves in a couple orders of magnitude less time than the benchmark approach by allowing multiple working and backup routes (we compare to one representative version of the traditional approach as our benchmark). This new mechanism and accompanying model will allow better benchmarking of SBPP-like network designs, and enhance further study into the performance of SBPP relative to other network survivability approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".