Probabilistic Fair Ordering of Events

TL;DR

This paper introduces Tommy, a probabilistic sequencing system that leverages statistical models of clock synchronization errors to order events fairly, overcoming the limitations of traditional timestamp-based methods.

Contribution

It proposes a novel probabilistic approach to event ordering that accounts for clock errors and applies social choice theory to produce fair partial orders.

Findings

Tommy outperforms baseline methods in fairness.

Probabilistic ordering handles clock errors effectively.

Partial orders improve fairness in event sequencing.

Abstract

A growing class of applications depends on fair ordering, where events that occur earlier should be processed before later ones. Providing such guarantees is difficult in practice because clock synchronization is inherently imperfect: events generated at different clients within a short time window may carry timestamps that cannot be reliably ordered. Rather than attempting to eliminate synchronization error, we embrace it and establish a probabilistically fair sequencing process. Tommy is a sequencer that uses a statistical model of per-clock synchronization error to compare noisy timestamps probabilistically. Although this enables ordering of two events, the probabilistic comparator is intransitive, making global ordering non-trivial. We address this challenge by mapping the sequencing problem to a classical ranking problem from social choice theory, which offers principled mechanisms for reasoning with intransitive comparisons. Using this formulation, Tommy produces a partial order of events, achieving significantly better fairness than a Spanner TrueTime-based baseline approach.