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.

翻译：循环执行的工业分析与机器学习工作流已成为现代工程实践中的主要计算负担。大规模参数库生成、周期性模型重训练、重复评估流水线以及广泛的超参数探索每次刷新周期可能消耗数百小时的运行时和数十千瓦时的电能，然而这些工作负载极少在显式能量感知下执行。我们提出CARINA（碳排放感知的循环工业分析），该测量与估算框架能够实现循环分析任务的能量感知与碳排放感知执行。该框架结合轻量级运行级与步骤级检测、高峰时段感知执行控制以及本地仪表盘报告功能。方法以能量负荷估算为主要目标，并利用本地电网排放因子将其转换为碳排放量，从而在缺乏设备级直接碳排放测量手段时仍可应用。我们使用两个汽车原始设备制造商的数据库生成工作流对框架进行评估。第一个工作流涉及148万场景、180.30小时和48.67千瓦时；第二个工作流涉及366万场景、274.75小时和74.16千瓦时（分别对应约21.8千克二氧化碳当量和33.2千克二氧化碳当量）。初步策略分析表明，基于峰时感知的非高峰时段加速可将全周期能量负荷降低约9%，同时产生约7%的运行时开销，而简单限速策略可能因开销效应导致总能耗增加。