Time, Causality, and Observability Failures in Distributed AI Inference Systems

TL;DR

This paper reveals that small clock skews in distributed AI inference systems can cause causality observability failures without affecting system correctness, emphasizing the importance of precise time synchronization.

Contribution

It demonstrates how minor clock skews lead to causality violations in distributed AI pipelines and highlights the need for better time management.

Findings

Causality violations occur at around 5 ms skew.

System throughput and correctness remain stable despite violations.

Violation behavior can stabilize or decrease over longer runs.

Abstract

Distributed AI inference pipelines rely heavily on timestamp-based observability to understand system behavior. This work demonstrates that even small clock skew between nodes can cause observability to become causally incorrect while the system itself remains functionally correct and performant. We present controlled experiments on a multi-node AI inference pipeline, where clock skew is introduced at a single stage. Results show that no violations are observed under synchronized conditions and up to 3 ms skew, while clear causality violations emerge by 5 ms. Despite this, system throughput and output correctness remain largely unaffected. We further observe that violation behavior is not strictly static. In longer runs, negative span rates may stabilize or decrease over time, indicating that effective skew evolves due to relative clock drift between nodes. Experiments were conducted using Kafka and ZeroMQ transports, with consistent results across both. Aeron is under active exploration but is not yet included in the completed validation set. These findings suggest that observability correctness depends not only on system functionality but also on precise time alignment, and that timing must be treated as a first-class concern in distributed AI systems.