ŶUGO: Enhancing Power Consumption Predictions in Data Centers through Combined Multiple Linear Regression and Gradient Boosting Techniques on Hypervisor Load

Accurate power consumption prediction in data centers is critical for optimizing energy efficiency, but is fundamentally challenged by workloads exhibiting both linear and non-linear patterns. This study proposes ŶUGO model, a novel two-stage hybrid model that integrates Gradient Boosting (GB) for non-linear workload forecasting and Multiple Linear Regression (MLR) for final power prediction, leveraging proxy metrics from hypervisor loads via SNMP. Through extensive experimentation, ŶUGO model demonstrates superior accuracy, achieving a Mean Absolute Error (MAE) of 1.46 and a Root Mean Squared Error (RMSE) of 1.50 in multi-step-ahead forecasting. Crucially, this performance represents a significant MAE reduction of up to 86.42% compared to the ARIMA model and substantial improvements over MLP and SVR benchmarks. The proposed framework offers a robust, scalable, and cost-effective solution for precise energy monitoring without requiring physical sensors, providing a practical tool for enhanced resource management in virtualized environments.