Kushal Babel (Cornell Tech & IC3), Andrey Chursin (Mysten Labs), George Danezis (Mysten Labs & University College London (UCL)), Anastasios Kichidis (Mysten Labs), Lefteris Kokoris-Kogias (Mysten Labs & IST Austria), Arun Koshy (Mysten Labs), Alberto Sonnino (Mysten Labs & University College London (UCL)), Mingwei Tian (Mysten Labs)

We introduce Mysticeti-C, the first DAG-based Byzantine consensus protocol to achieve the lower bounds of latency of 3 message rounds.
Since Mysticeti-C is built over DAGs it also achieves high resource efficiency and censorship resistance. Mysticeti-C achieves this latency improvement by avoiding explicit certification of the DAG blocks and by proposing a novel commit rule such that every block can be committed without delays, resulting in optimal latency in the steady state and under crash failures. We further extend Mysticeti-C to Mysticeti-FPC, which incorporates a fast commit path that achieves even lower latency for transferring assets. Unlike prior fast commit path protocols, Mysticeti-FPC minimizes the number of signatures and messages by weaving the fast path transactions into the DAG. This frees up resources, which subsequently result in better performance. We prove the safety and liveness in a Byzantine context. We evaluate both Mysticeti protocols and compare them with state-of-the-art consensus and fast path protocols to demonstrate their low latency and resource efficiency, as well as their more graceful degradation under crash failures. Mysticeti-C is the first Byzantine consensus protocol to achieve WAN latency of 0.5s for consensus commit while simultaneously maintaining state-of-the-art throughput of over 100k TPS. Finally, we report on integrating Mysticeti-C as the consensus protocol into a major deployed blockchain, resulting in over 4x latency reduction.

View More Papers

Passive Inference Attacks on Split Learning via Adversarial Regularization

Xiaochen Zhu (National University of Singapore & Massachusetts Institute of Technology), Xinjian Luo (National University of Singapore & Mohamed bin Zayed University of Artificial Intelligence), Yuncheng Wu (Renmin University of China), Yangfan Jiang (National University of Singapore), Xiaokui Xiao (National University of Singapore), Beng Chin Ooi (National University of Singapore)

Read More

BULKHEAD: Secure, Scalable, and Efficient Kernel Compartmentalization with PKS

Yinggang Guo (State Key Laboratory for Novel Software Technology, Nanjing University; University of Minnesota), Zicheng Wang (State Key Laboratory for Novel Software Technology, Nanjing University), Weiheng Bai (University of Minnesota), Qingkai Zeng (State Key Laboratory for Novel Software Technology, Nanjing University), Kangjie Lu (University of Minnesota)

Read More

Beyond Classification: Inferring Function Names in Stripped Binaries via...

Linxi Jiang (The Ohio State University), Xin Jin (The Ohio State University), Zhiqiang Lin (The Ohio State University)

Read More

How Different Tokenization Algorithms Impact LLMs and Transformer Models...

Ahmed Mostafa, Raisul Arefin Nahid, Samuel Mulder (Auburn University)

Read More