When Honest Nodes in PBFT Consensus Meet Software Aging: SMP-Based Performability Evaluation
Bibliographic record
Abstract
Availability and/or performance of PBFT (Practical Byzantine Fault Tolerance) consensus service has been widely studied. However, the existing studies overlook the situation of software aging of honest nodes, which can degrade system performance over time. Rejuvenation techniques can mitigate the negative impact of aging. This paper aims to make a quantitative joint analysis of availability and performance (a.k.a performability) of PBFT consensus service in the scenario where honest nodes are susceptible to software aging and rejuvenation techniques are adopted for recovery. We propose a Semi-Markov process (SMP) based approach for model-based evaluation. Unlike traditional models that rely on exponential distributions, our approach allows the time intervals of all events to follow general distributions, thereby enable a more nuanced analysis of PBFT dynamics. We detail the modeling process and the derivation of metric formulas. We also carry out numerical analysis for the evaluation to assess the performability of PBFT consensus service.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".