Chronos: An Efficient Asynchronous Byzantine Ordered Consensus
Bibliographic record
Abstract
Abstract Byzantine ordered consensus, introduced by Zhang et al. (OSDI 2020), is a new consensus primitive that additionally guarantees a correctness specification of transaction order, allowing nodes to assign fairly an ordering indicator to the committed transaction. Zhang et al. also presented a concrete Byzantine ordered consensus protocol called Pompē in the partially synchronous network model. However, Pompē cannot prevent an adversary from manipulating message delivery time. In this paper, we present Chronos, the first Byzantine ordered consensus protocol in the asynchronous network model, where an adversary can arbitrarily manipulate message delivery time. To construct Chronos, we propose a variant of asynchronous common subset called signal asynchronous common subset protocol, which guarantees the liveness of Chronos. We implement both Chronos and its baseline HoneyBadgerBFT using Go language and deploy them on 100 Amazon t3.medium instances distributed throughout 10 regions across the world. The experimental results show that Chronos is more efficient than HoneyBadgerBFT for small network, achieving peak throughput of 59 368 tx/s when the batch size is 100 000 and the number of nodes is 4, while the peak of HoneyBadgerBFT is 57 077 tx/s.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".