🤖 AI Summary
Asynchronous Byzantine Fault Tolerant (BFT) consensus has long struggled to simultaneously achieve high throughput and low latency: traditional chain-based protocols (e.g., PBFT) offer low latency but suffer from throughput bottlenecks, whereas emerging DAG-based BFT protocols improve throughput at the cost of significantly increased latency. This paper proposes a novel DAG-BFT protocol that decouples message propagation from consensus decision-making, enables parallel proposal generation by multiple nodes, and introduces a lightweight causal-order verification mechanism. For the first time in leaderless DAG-BFT, it achieves an average message hop count of 4.5 for transaction finality. Experimental results show up to 60% reduction in end-to-end latency while matching state-of-the-art DAG-BFT throughput. The protocol provides formally proven safety, liveness, and rapid fault recovery—approaching the theoretical optimum in the BFT latency-throughput trade-off.
📝 Abstract
Today's practical partially synchronous Byzantine Fault Tolerant (BFT) consensus protocols trade off low latency and high throughput. On the one end, traditional BFT protocols such as PBFT and its derivatives optimize for latency. They require, in fault-free executions, only 3 message exchanges to commit, the optimum for BFT consensus. However, this class of protocols typically relies on a single leader, hampering throughput scalability. On the other end, a new class of so-called DAG-BFT protocols demonstrates how to achieve highly scalable throughput by separating data dissemination from consensus, and using every replica as proposer. Unfortunately, existing DAG-BFT protocols pay a steep latency premium, requiring on average 10.5 message exchanges to commit a transactions. This work aims to soften this tension and proposes Shoal++, a novel DAG-based BFT consensus system that offers the throughput of DAGs while reducing commit latency to an average of 4.5 message exchanges. Our empirical findings are encouraging, showing that Shoal++ achieves throughput comparable to state-of-the-art DAG BFT solutions while reducing latency by up to 60%.