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

• URL: http://arxiv.org/abs/2509.09991v1
• Date: Fri, 12 Sep 2025 06:22:01 GMT
• Title: Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning
• Abstract summary: We train a Gradient Boosting Regressor to predict energy consumption measured via RA interfaces on the host.<n>We demonstrate for the first time, guest-only resource-based energy estimation without privileged host access.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 ($0.90 \leq R^2 \leq 0.97$), 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.

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