This study presents a comparative evaluation of machine learning and temporal models for predicting power consumption in data centers. Using real-world Google cluster trace data comprising 8,927 temporally ordered samples, we investigate the effectiveness of multiple predictive approaches, including naive temporal baselines, linear regression, Random Forest, and classical time-series models such as AR(1) and ARIMA. The dataset is constructed using aggregated 5-minute intervals with features including CPU utilization, lagged CPU values, lagged power, and derived feature transformations. A time-based train-test split is employed to preserve temporal dependencies and avoid data leakage. Model performance is evaluated using Root Mean Squared Error (RMSE). Experimental results show that a simple naive temporal model achieves the lowest prediction error, outperforming both machine learning and classical time-series approaches. Linear regression demonstrates comparable performance, while Random Forest slightly underperforms. Traditional time-series models and CPU-only models exhibit significantly higher error, indicating limited predictive capability. These findings suggest that temporal stability plays a dominant role in data center power behavior, particularly in low-utilization environments, and that workload features alone provide limited predictive value. The study highlights the importance of baseline temporal patterns over model complexity for accurate power prediction.
DUBEY et al. (Thu,) studied this question.