Improved availability models for p-cycle-based network design
Bibliographic record
Abstract
Dual-failures are considered as the main contributors to service unavailability in p-cycle based mesh networks that are designed to withstand single failures. Methods such as post-failure reconfiguration and pre-failure provisioning of additional protection capacity have been considered to add another level of protection against dual-failures. In this paper, we present availability-aware service provisioning method in networks designed to only withstand single failures. The approach we discuss builds upon previous work and uses the concept of ldquocutsets method" to categorize failures that cause overall service outage; we discuss some subtle issues which make existing methods inaccurate. We then develop an improved non-joint optimization ILP model for solving the service provisioning problem under the assumption of fully loaded straddling spans in p-cycles. We also address the scalability issue by introducing several techniques to speed up the run time of the model. We evaluate the resources of inaccuracy in different scenarios. Our results indicate that the ILP solutions of our models outperform the prior work in terms of estimating service path unavailability in all considered network and traffic scenarios.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".