A Performance Comparison of Algorithms for Byzantine Agreement in Distributed Systems
Bibliographic record
Abstract
Reaching agreement in the presence of byzantine processes is an important task in distributed systems. Theoretical analysis of algorithms for Byzantine Agreement can provide insight into their efficiency. However, analysis of algorithms under varying parameters and practical constraints through experimental evaluation can be key to understanding the performance and trade-offs of theoretically well-performing algorithms. We compare the performance of two randomized byzantine agreement algorithms-one using the pull-push approach and another using the concept of quorums-and a third recent simple deterministic byzantine agreement algorithm. Through implementation on a testbed environment using the metrics of bit complexity, round complexity and latency in the presence of network sizes and faulty processes, we quantify the performance of each algorithm. In terms of bit complexity, we show that for small networks (n <; 32) and up to 10% faulty processes, the simple deterministic algorithm performs best, while for larger networks, pull-push is the best performing algorithm. The second randomized algorithm performs best in terms of latency.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".