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GradedDAG: An Asynchronous DAG-based BFT Consensus with Lower Latency

Xiaohai Dai, Z. Zhang, Jiang Xiao, J. F. Yue, Xia Xie, Hai Jin

202318 citationsDOI

Abstract

To enable parallel processing, the Directed Acyclic Graph (DAG) structure is introduced to the design of asyn-chronous Byzantine Fault Tolerant (BFT) consensus protocols, known as DAG-based BFT. Existing DAG-based BFT protocols operate in successive waves, with each wave containing three or four Reliable Broadcast (RBC) rounds to broadcast data, resulting in high latency due to the three communication steps required in each RBC. For instance, Tusk, a state-of-the-art DAG-based BFT protocol, has a good-case latency of 7 communication steps and an expected worst latency of 21 communication steps. To reduce latency, we propose GradedDAG, a new DAG-based BFT consensus protocol based on our adapted RBC called Graded RBC (GRBC) and the Consistent Broadcast (CBC), with each wave consisting of only one GRBC round and one CBC round. Through GRBC, a replica can deliver data with a grade of 1 or 2, and a non-faulty replica delivering the data with grade 2 can ensure that more than 2/3 of replicas have delivered the same data. Meanwhile, through CBC, data delivered by different non-faulty replicas must be identical. In each wave, a block in the GRBC round will be elected as the leader. If a leader block has been delivered with grade 2, it and all its ancestor blocks can be committed. GradedDAG offers a good-case latency of 4 communication steps and an expected worst latency of 7.5 communication steps, significantly lower than the state-of-the-art. Experimental results demonstrate GradedDAG's feasibility and efficiency.

Topics & Concepts

Latency (audio)Computer scienceAsynchronous communicationDirected acyclic graphReplicaComputer networkAtomic broadcastBroadcast communication networkDistributed computingParallel computingBroadcasting (networking)AlgorithmTelecommunicationsArtVisual artsDistributed systems and fault toleranceAdvanced Data Storage TechnologiesCognitive Functions and Memory