State-Machine Replication Scalability Made Simple (Extended Version)

Consensus, state-machine replication (SMR) and total order broadcast (TOB) protocols are notorious for being poorly scalable with the number of participating nodes. Despite the recent race to reduce overall message complexity of leader-driven SMR/TOB protocols, scalability remains poor and the throu...

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Bibliographic Details
Main Authors Stathakopoulou, Chrysoula, Pavlovic, Matej, Vukolić, Marko
Format Journal Article
LanguageEnglish
Published 10.03.2022
Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
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Summary:Consensus, state-machine replication (SMR) and total order broadcast (TOB) protocols are notorious for being poorly scalable with the number of participating nodes. Despite the recent race to reduce overall message complexity of leader-driven SMR/TOB protocols, scalability remains poor and the throughput is typically inversely proportional to the number of nodes. We present Insanely Scalable State-Machine Replication, a generic construction to turn leader-driven protocols into scalable multi-leader ones. For our scalable SMR construction we use a novel primitive called Sequenced (Total Order) Broadcast (SB) which we wrap around PBFT, HotStuff and Raft leader-driven protocols to make them scale. Our construction is general enough to accommodate most leader-driven ordering protocols (BFT or CFT) and make them scale. Our implementation improves the peak throughput of PBFT, HotStuff, and Raft by 37x, 56x, and 55x, respectively, at a scale of 128 nodes.
DOI:10.48550/arxiv.2203.05681