微小时间偏差可导致系统观测失效，但不影响实际性能。
Time, Causality, and Observability Failures in Distributed AI Inference Systems
- 在多节点推理系统中引入时钟偏移，测试其对可观测性的影响。
- 时钟偏移达5毫秒时出现明显因果错误，但吞吐量和输出正确性不变。
- 适合关注分布式系统可观测性与时间同步的工程师与研究者。
分布式AI推理流水线严重依赖基于时间戳的可观测性来理解系统行为。本工作表明，即使节点间存在微小的时钟偏移，也可能导致可观测性出现因果错误，而系统本身仍保持功能正确且性能良好。我们在多节点推理管道上进行了受控实验，仅在一个阶段引入时钟偏移。结果表明，在同步条件下及最多3毫秒偏移时未观察到任何违规；而当偏移达到5毫秒时，明显的因果性违规开始出现。尽管如此，系统吞吐量和输出正确性基本不受影响。我们还发现，违规行为并非静态：在长时间运行中，负跨度率可能趋于稳定或下降，表明有效偏移随节点间相对时钟漂移动态演化。实验使用Kafka和ZeroMQ传输协议，结果一致；Aeron正在探索中，尚未纳入完整验证集。这些发现表明，可观测性正确性不仅依赖于系统功能，还取决于精确的时间对齐，因此时间必须作为分布式AI系统中的首要考量因素。
原文摘要 · Abstract (English)
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.
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