Abstract:Recurring industrial analytics and machine-learning workflows are becoming a major computational burden in modern engineering practice. Large parametric database generation, scheduled model retraining, repeated evaluation pipelines, and extensive hyperparameter exploration can demand hundreds of runtime hours and tens of kilowatt-hours per refresh cycle, yet these workloads are rarely executed with explicit energy-awareness. We present CARINA (Carbon-Aware Recurrent Industrial Analytics), a measurement-and estimation framework for energy-aware and carbon-aware execution of recurrent analytics. The framework combines lightweight run-level and step-level instrumentation, peak time-aware execution control, and local dashboard reporting. The method estimates energy load as the primary objective and translates it to carbon emissions using a local grid emission factor, enabling use even when direct device level carbon metrology is unavailable. We evaluate the framework using two automotive OEM database-generation workflows. The first required 1.48 million scenarios, 180.30 h, and 48.67 kWh; the second required 3.66 million scenarios, 274.75 h, and 74.16 kWh (corresponding to approximately 21.8 kg CO2e and 33.2 kg CO2e, respectively). Preliminary policy analysis suggests that peak-aware off-hours boosting can reduce full-cycle energy load by about 9% with roughly 7% runtime overhead, while naive throttling can increase total energy through overhead effects.
| Subjects: | Performance (cs.PF) |
| Cite as: | arXiv:2605.24561 [cs.PF] |
| (or arXiv:2605.24561v1 [cs.PF] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24561 arXiv-issued DOI via DataCite (pending registration) |
From: Muhammad Umar Farooq [view email]
[v1]
Sat, 23 May 2026 12:56:29 UTC (184 KB)
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