Simulation relations for fault-tolerance
Bibliographic record
Abstract
Abstract We present a formal characterization of fault-tolerant behaviors of computing systems via simulation relations. This formalization makes use of variations of standard simulation relations in order to compare the executions of a system that exhibits faults with executions where no faults occur; intuitively, the latter can be understood as a specification of the system and the former as a fault-tolerant implementation. By employing variations of standard simulation algorithms, our characterization enables us to algorithmically check fault-tolerance in polynomial time, i.e., to verify that a system behaves in an acceptable way even subject to the occurrence of faults. Furthermore, the use of simulation relations in this setting allows us to distinguish between the different levels of fault-tolerance exhibited by systems during their execution. We prove that each kind of simulation relation preserves a corresponding class of temporal properties expressed in CTL; more precisely, masking fault-tolerance preserves liveness and safety properties, nonmasking fault-tolerance preserves liveness properties, while failsafe fault-tolerance guarantees the preservation of safety properties. We illustrate the suitability of this formal framework through its application to standard examples of fault-tolerance.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".