仅用虚拟机资源数据，就能精准估算其能耗。
Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning
- 基于虚拟机资源指标，用梯度提升回归预测能耗。
- 在多种工作负载下，准确率高达R²=0.90~0.97。
- 适合云环境中的节能调度与成本优化场景。
本文提出一种基于机器学习的虚拟服务器能耗估算方法，无需访问物理电源接口。通过收集来自客户虚拟机的资源利用率指标，训练梯度提升回归模型，预测宿主机上通过RAPL测量的实际能耗。首次在无特权宿主机访问条件下，实现仅依赖客户机资源数据的能耗估算，覆盖多种工作负载，预测准确率高，方差解释率在0.90至0.97之间，证明了客端能耗估算的可行性。该方法可支持虚拟化环境中能源感知调度、成本优化及与物理宿主机无关的能耗评估，填补了云等虚拟化环境无法直接测量能耗的关键空白。
原文摘要 · Abstract (English)
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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