JUNO: Aggregated Vector Consensus for Optimal Asynchronous Common Subset

2026-07-31

AI Summary

This work addresses the lack of efficient and practical vector consensus protocols in asynchronous networks, which has constrained the performance of Asynchronous Common Subset (ACS). The paper introduces an aggregated vector consensus primitive and, for the first time, applies it in a fully asynchronous setting to construct the JUNO protocol, achieving ACS with optimal $O(n^2)$ message complexity. Experimental evaluations demonstrate that JUNO improves average throughput by 93% over HoneyBadgerBFT and by 47% over Dory, thereby substantiating its high efficiency and practicality. This advancement effectively fills the gap in high-performance vector consensus protocols for such environments.

Abstract

In this paper, we propose \textit{aggregated vector consensus}, a new vector consensus primitive designed for asynchronous networks. The primitive achieves agreement by outputting a vector of values aggregated from independent process inputs. We then introduce \textsc{Juno}, an asynchronous common subset (ACS) protocol that fully implements our aggregated vector consensus to attain optimal $\mathcal{O}(n^2)$ message complexity. We further implement and evaluate \textsc{Juno} in comparison with the legacy HoneyBadgerBFT and the state-of-the-art Dory. Experiment results demonstrate its efficacy and efficiency. Our protocol demonstrates an average throughput performance improvement of 93\% compared with HoneyBadgerBFT and a 47\% improvement compared with Dory. Notably, our study makes significant progress in addressing the gap in applying vector consensus protocol in fully asynchronous networks.

Problem

• vector consensus
• asynchronous networks
• common subset
• message complexity
• distributed agreement

Innovation

• aggregated vector consensus
• asynchronous common subset
• message complexity
• Juno
• asynchronous networks