Performance Modeling and Analysis of Hotstuff for Blockchain Consensus
Bibliographic record
Abstract
Byzantine Fault-Tolerant (BFT) protocols are classical algorithms that offer a faster and more energy-efficient consensus mechanism compared to Proof-of-Work (PoW), which is typically used by cryptocurrencies such as Bitcoin. Synchronous BFT systems are hard to implement and vulnerable to attacks that aim to disrupt the synchrony of the system. Practical BFT (PBFT), which is a partially synchronous protocol, is a high-performance consensus algorithm that provides strong safety in the presence of a bounded number of faulty participants. Hotstuff is one such partially synchronous BFT State Machine Replication (SMR) protocol that aims to address the aforementioned issues. PBFT is becoming a popular choice for blockchain consensus, especially in permissioned systems (e.g. Ripple, Stellar, etc). However, it is not well understood how Hotstuff, and PBFT consensus in general, behave under varying conditions that are commonly found in blockchain networks. In this paper, we present a theoretical model for the Hotstuff consensus mechanism which accurately predicts blockchain-related metrics such as the transaction throughput and expected confirmation time using important networking parameters such as the number of replicas, link latency, and packet loss. Furthermore, we validate our model through extensive simulations carried out using OMNeT++. Our results show that Hotstuff performance degrades drastically when the number of replicas increases. We observe that with a maximum number of tolerable faulty nodes, when the number of validators is increased to 127, throughput tends to zero. As well, packet loss ratio and transaction processing time are two other factors that significantly affect the performance of Hotstuff.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".