This paper presents a machine learning-based approach to estimate the energy consumption of virtual servers without access to physical power measurement interfaces. Using resource utilization metrics collected from guest virtual machines, we train a Gradient Boosting Regressor to predict energy consumption measured via RAPL on the host. We demonstrate, for the first time, guest-only resource-based energy estimation without privileged host access with experiments across diverse workloads, achieving high predictive accuracy and variance explained (0.90≤R2≤0.97), indicating the feasibility of guest-side energy estimation. This approach can enable energy-aware scheduling, cost optimization and physical host independent energy estimates in virtualized environments. Our approach addresses a critical gap in virtualized environments (e.g. cloud) where direct energy measurement is infeasible.
@article{arxiv.2509.09991,
title = {Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning},
author = {Amandip Sangha},
journal= {arXiv preprint arXiv:2509.09991},
year = {2025}
}