Latency-Optimal View Synchronization for 2f+1 BFT Consensus in the Presence of Faults

Master Thesis (2026)
Author(s)

D. Lihotský (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Jérémie Decouchant – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)

H.J. Griffioen – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Publication Year
2026
Language
English
Graduation Date
14-07-2026
Awarding Institution
Delft University of Technology
Project
CS5000 Master Thesis
Programme
Computer Science
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

Byzantine fault-tolerant (BFT) consensus underpins modern state machine replication and blockchain systems. Recent consensus protocols reduce the classical 3f+1 replica requirement to 2f+1 by leveraging trusted execution environments, but view synchronization in this setting has received little systematic attention and typically relies on exponential timeout backoff, which has unbounded worst-case latency. On the other hand, in the 3f+1 setting, recent view synchronization work has focused on minimizing asymptotic message complexity, with limited consideration of the latency cost this imposes. This work addresses both gaps. We first adapt the message-optimal LP22 and Lumiere synchronizers to the 2f+1 setting. We then design two latency-optimized synchronizers tailored to the 2f+1 setting: a broadcast-based protocol and a leader-based protocol, which we call Babette. We implemented all four algorithms on top of the OneShot consensus protocol and evaluated them on the DAS-5 cluster across network sizes up to n=51 and fault scenarios ranging from no failures to the maximum tolerated f. With f failures, Babette achieves around 5.5× higher throughput and correspondingly lower latency than Lumiere across all tested network sizes, demonstrating that asymptotic message complexity-optimality does not immediately translate to performance improvement when timeout constants dominate end-to-end performance.

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