Walnut: A Generic Framework with Enhanced Scalability for BFT Protocols


연구 분야: Networking



학회: Australasian Conference on Information Security and Privacy


초록

The performance of traditional BFT protocols significantly decreases as n grows (n for the number of replicas), and thus, they support up to a few hundred replicas. Such scalability issues severely limit the application scenarios of BFT. Meanwhile, the committee sampling technique has the potential to scale the replica size significantly by selecting a small portion of replicas as the committee and then conveying the consensus results to the rest. However, this technique is rarely used in BFT, and there is still a lack of methods to scale the traditional BFT protocol being deployed to support more replicas rather than the costly re-deployment of new protocols. This paper introduces Walnut, a secure and generic committee-sampling-based modular consensus. Specifically, we use the verifiable random function for committee election and integrate committee rotation with the consensus. This resulting construction ensures that each selected committee is of a fixed size and acknowledged by all replicas, even in a partially synchronous network. For Walnut, we provide a rigorous definition and outline the necessary properties of each module to achieve safety and liveness. To clarify Walnut’s effectiveness, we apply this framework to HotStuff to obtain the Walnut-HS protocol, together with a proof of fit-in. We also implement Walnut-HS and compare its performance with HotStuff, using up to 100 Amazon EC2 instances in WAN. The experiments show that Walnut-HS can easily scale to 1,000 replicas with only a slight performance degradation, while HotStuff performs poorly or even breaks when \(n\!>\!200\). Besides, Walnut-HS performs well in comparison with Hotstuff for small-scale experiments. For example, the peak throughput of Walnut-HS is at most 38.6% higher than HotStuff for \(n\!=\!100\).


Author Profile
Lei Tian

School of Computer Science Shanghai Jiao Tong University Shanghai China

China
Author Profile
Chenke Wang

School of Computer Science Shanghai Jiao Tong University Shanghai China

China
Author Profile
Yu Long

School of Computer Science Shanghai Jiao Tong University Shanghai China

China

📄 논문 정보

발행 연도 2025년
인용수 0
출판 국가 Andorra, China
사이트 Springer
좋아요 수 0

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