Edge-cloud collaboration-driven predictive modeling for high-performance computing centers.

Abstract

Accurate coolant flow prediction is critical for active thermal management in high-performance computing (HPC) centers, yet it is inherently challenged by mixed-timescale dynamics and high-frequency workload surges. Existing deep learning methods often prioritize global accuracy on smoothed stationary trends, which may lead to phase delays during abrupt thermal transients. In addition, high-capacity architectures can introduce non-negligible computational overhead for latency-sensitive, resource-constrained edge controllers. To overcome these limitations, this study proposes a deployment-oriented edge-cloud collaboration (ECC) framework integrated with a transient-aware predictive architecture, named FS-Attention, designed to balance transient responsiveness, engineering deployability, and decision transparency. FS-Attention couples local feature synthesis, temporal-memory encoding, and attention-based temporal refinement to improve coolant-flow tracking under non-stationary operating conditions. Evaluations on the real-world Frontier supercomputer dataset show that the feature synthesis attention (FS-Attention) model achieves competitive full-year prediction accuracy, with a coefficient of determination (R²) of 0.8744 and a root mean square error (RMSE) of 0.0350. Under isolated critical thermal events (CTEs), FS-Attention obtains the lowest RMSE of 0.0757, slightly lower than the Temporal Fusion Transformer (TFT) and 5.61% lower than the standard Transformer. Platform-based profiling further shows an inference latency of 0.016 ms and a parameter size of 323.1 K, suggesting model-side compatibility with facility-side edge execution, while attention-shift analysis provides diagnostic evidence of temporally adaptive model behavior under dynamic thermal conditions.