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Machine learning estimates virtual server energy use from internal metrics

By PulseAugur Editorial · [1 sources] ·

A new research paper explores using machine learning to estimate the energy consumption of virtual servers. The study proposes a method that predicts CPU-package power attributable to a virtual server using system and performance metrics observable within the server itself. While the approach shows strong prediction accuracy when training data covers the deployment conditions, its reliability decreases when transferring to unseen workloads, hardware, or configurations. AI

IMPACT This research could lead to more accurate energy monitoring for cloud infrastructure, potentially optimizing resource allocation and reducing operational costs.

RANK_REASON Academic paper on a novel machine learning approach for energy estimation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning estimates virtual server energy use from internal metrics

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Academic paper on a novel machine learning approach for energy estimation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Amandip Sangha ·

    Energy Estimation for Virtual Servers Using Resource Utilization and Machine Learning

    arXiv:2509.09991v2 Announce Type: replace Abstract: Estimating the energy use of virtual servers is difficult because physical power interfaces and privileged host counters are typically accessible only on the underlying physical host. We investigate a machine learning approach t…

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