CENTILE: A Telemetry Foundation Model Evaluated by the Decisions It Drives

2026-08-03

AI Summary

This work addresses the limitations of traditional operations that rely on multiple isolated predictors for heterogeneous telemetry data, which hinder unified modeling and degrade decision-making performance. The paper proposes the first generative telemetry foundation model tailored for HPC job scheduling and network resource allocation. Treating telemetry as an event-driven stream of irregular temporal entities, the model employs a single-pass inference architecture that requires no future timestamps and incorporates calibrated conditional quantile forecasting. This design enables flexible time-horizon predictions and zero-shot transfer across time intervals, achieving effective cross-domain adaptation with only a few hours of target-domain data. Replay experiments on real-world HPC job logs and network traffic demonstrate that the approach reduces average bounded slowdown by 77% and cuts policy violation rates by approximately 50%.

Abstract

Modern computing and networking infrastructure emits telemetry continuously, yet operators convert it into decisions with a separate predictor per task, entity, and horizon. One generative model, pretrained once over an operator's own event streams, could replace this fleet, an approach that already scales to high-cardinality streams in recommendation systems. However, point-forecast error on operational telemetry saturates near simple last-value baselines, so lower error alone need not improve the decisions it feeds. To close this gap, we present \sys, a generative foundation model for network and systems telemetry, evaluated by replaying the decisions its calibrated conditional quantiles drive. \sys treats heterogeneous telemetry as event-driven, irregularly timed entity streams and serves flexible forecast horizons in a single pass, requiring no future timestamps. To our knowledge, \sys is the first pretrained telemetry model to improve both HPC scheduling and network provisioning decisions under replay, its runtime estimator transferring zero-shot across months and its pretrained weights across domains from hours of target data. Extensive experiments on HPC job logs and network traffic confirm that \sys lowers the mean bounded slowdown of backfilling by up to approximately $77\%$ over deployed user estimates and roughly halves the deployed rule's violation rate. Our code is available at https://github.com/ZzZTripleZzZ/all-in-one.

Problem

• telemetry
• decision-making
• foundation model
• forecasting
• systems monitoring

Innovation

• foundation model
• telemetry
• decision-driven evaluation
• conditional quantiles
• zero-shot transfer