Modern distributed systems rely on consensus protocols to build a fault-tolerant-core upon which they can build applications. Consensus protocols are correct under a specific failure model, where up to f machines can fail. We argue that this f-threshold failure model oversimplifies the real world and limits potential opportunities to optimize for cost or performance. We argue instead for a probabilistic failure model that captures the complex and nuanced nature of faults observed in practice. Probabilistic consensus protocols can explicitly leverage individual machine \textit{failure curves} and explore side-stepping traditional bottlenecks such as majority quorum intersection, enabling systems that are more reliable, efficient, cost-effective, and sustainable.
@article{arxiv.2602.11362,
title = {Real Life Is Uncertain. Consensus Should Be Too!},
author = {Reginald Frank and Soujanya Ponnapalli and Octavio Lomeli and Neil Giridharan and Marcos K Aguilera and Natacha Crooks},
journal= {arXiv preprint arXiv:2602.11362},
year = {2026}
}
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