AI summaryⓘ
The authors studied how long it takes for new blocks to be confirmed in fast blockchain systems that use a method called BFT consensus. They found that the times vary a lot because of different network conditions and how the validators (computers that check transactions) are set up. By using a special statistical model that mixes different network scenarios, they could explain these variations better. They tested this on two real blockchains, Hyperliquid and Aptos, showing that Hyperliquid has more uniform validator setups, while Aptos shows more complex patterns due to different network paths and a recent update. This method helps understand and monitor how these blockchains perform under real conditions.
Byzantine Fault ToleranceBFT consensusblock time distributionsquorum formationmixture modelsnetwork heterogeneityHyperliquidAptosmultimodal distributionconsensus upgrade
Authors
Hongru He, Akihiro Fujihara
Abstract
High-performance Byzantine Fault Tolerant (BFT) blockchains are designed to achieve high throughput and low latency, yet their observed block time distributions often reveal complex behaviors arising from networking, pipelining, and deployment heterogeneity. In this paper, we diagnose HotStuff-based high-performance BFT consensus by modeling block times through a quorum-based multicast framework that links each block interval to quorum formation latency. We capture multimodal block time distributions using mixture models, where each component represents a distinct network condition characterized by effective transfer rate of block information. The proposed model is fitted to the bulk of mainnet block time data, while tail decay is analyzed separately to assess asymptotic behavior. Applying this methodology to Hyperliquid and Aptos mainnets, we find that Hyperliquid is well explained by a unimodal distribution, consistent with a relatively homogeneous validator deployment. In contrast, Aptos exhibits persistent multimodal structure and a pronounced shift following a consensus upgrade, reflecting heterogeneous deployments and diverse communication paths. These results demonstrate that mixture modeling of block time provides a practical and informative diagnostic tool for analyzing and monitoring high-performance BFT consensus.