Reliability and Criticality Analysis of Communication Networks by Stochastic Computation
Bibliographic record
Abstract
Reliability is an important feature in the design and maintenance of a large-scale network. In this article, the reliability of information transmission between a transmitter and a receiver (i.e., a two-terminal network) is considered as a generalized connectivity framework of terminal nodes. As network complexity increases, existing approaches to reliability analysis are encountering significant challenges. In this article, stochastic computational models are presented to efficiently analyze the reliability and criticality of a two-terminal network. Non-Bernoulli sequences with fixed numbers of 1s and 0s are utilized to encode the signal probabilities, and improve computational efficiency and accuracy. Both unidirectional and bidirectional links are considered for the probabilistic information transition process by imperfect links. Imperfect nodes are also modeled by the stochastic model of an imperfect unidirectional link. Non-exponential failure distributions and correlated signals in a two-terminal network are readily handled by the stochastic approach. The reliability of a system with external deterministic failures on a link is compared to that of the system prior to the occurrence of the failures. The difference in reliability is referred to as the criticality of the link. An analysis is pursued for the critical links based on the value of criticality. The proposed approach can be used to analyze and improve network reliability when utilizing limited redundancy for protecting the links.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".