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Accurate Distributed Tracing for Large-Scale AI Infrastructure: Time Synchronization as a Foundation for Reliable Observability — arXiv:2609.23301

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.23301
License (as archived): CC0-1.0
Archived: 2026-10-09T06:53:02.034Z
SHA-256 of the archived text: d64dae113f40df5fc0df43d547c68b8963301f8b41d18de33bdfed2cabe0811f
Stable URL of this page: https://metraipolis.com/documento/dc_8a51656ef676

How to cite

Accurate Distributed Tracing for Large-Scale AI Infrastructure: Time Synchronization as a Foundation for Reliable Observability — arXiv:2609.23301 — arXiv (en), https://arxiv.org/abs/2609.23301 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8a51656ef676 (Metraipolis archive dc_8a51656ef676, sha256 d64dae113f40df5fc0df43d547c68b8963301f8b41d18de33bdfed2cabe0811f)

BibTeX

@misc{dc8a51656ef676,
  title = {Accurate Distributed Tracing for Large-Scale AI Infrastructure: Time Synchronization as a Foundation for Reliable Observability — arXiv:2609.23301},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.23301},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8a51656ef676; sha256 d64dae113f40df5fc0df43d547c68b8963301f8b41d18de33bdfed2cabe0811f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.23301. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Distributed tracing in large-scale AI infrastructure fails silently when clock accuracy is insufficient: causal events are misordered, fault attribution is corrupted, and performance diagnoses are unreliable. We present TempoTrace, a system that co-designs IEEE 1588v2 PTP time synchronization with distributed tracing to preserve causal ordering across heterogeneous, multi-tenant GPU clusters. We formally prove that NTP-grade clocks produce causal inversions at 25-30% of directed operation pairs under WAN/cloud conditions (11.3% under LAN NTP). A Laplace heavy-tail noise model extends the analysis beyond Gaussian assumptions, giving a weighted expected misorder rate of 27.76% vs. 29.37% Gaussian, confirming robustness to tail shape. TempoTrace reduces GPU-to-host timestamp uncertainty from 2.1 us to a design target of 0.056 us residual standard deviation via GPUDirect RDMA hardware timestamping, with sub-100 ns PTP synchronization. A bounded Sketch Vector Clock tracks causal relationships with false-positive rate below 10^-5 for up to four participants. A hybrid rule-based and XGBoost diagnosis engine achieves macro-F1 0.974 in controlled validation (1,800-incident corpus; McNemar p=1.25e-23). A formally proven multi-tenant model provides isolated logical clock domains. For RoCEv2/ECMP fabrics, P4-based in-band network telemetry corrects path-delay asymmetry, yielding 99.1% attribution accuracy. Application Confidence Policies let workloads specify precision requirements and degraded-mode fallback. Evaluation combines physical five-node measurements (NTP inversion rate 44.978%, Laplace fit preferred over Gaussian by AIC under congestion) and controlled synthetic validation with illustrative configurations from 512 to 16,384 H100 GPUs across InfiniBand and RoCEv2 fabrics.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-22 in cs.DC).
Categorie arXiv: Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2609.23301 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T06:50:13.528Z dalla pagina https://arxiv.org/catchup/cs.DC/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).