Real Life Is Uncertain. Consensus Should Be Too!

TL;DR

This paper advocates for adopting a probabilistic failure model in distributed consensus protocols, moving beyond traditional fixed-threshold models to better reflect real-world fault complexities, enabling more reliable and efficient systems.

Contribution

It introduces a probabilistic failure model for consensus protocols, allowing for optimization based on individual machine failure behaviors rather than fixed failure thresholds.

Findings

Probabilistic models better capture real-world fault behaviors.

Potential to improve system reliability and efficiency.

Enables bypassing traditional quorum intersection bottlenecks.

Abstract

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.