# Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning

arXiv: https://arxiv.org/abs/2509.09991 · HTML: https://mlsanity.com/paper/2509.09991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
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- First posted on arXiv 12 Sep 2025.

## Abstract (arXiv)

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.