Idea
Machine learning platform estimating virtual server energy consumption from guest metrics for cloud operators and data centers
Research Paper
Core Innovation
This paper introduces a novel method to estimate virtual server energy consumption solely from guest VM resource metrics without requiring host-level power data. It uses a Gradient Boosting Regressor trained on host RAPL measurements to achieve high accuracy. This enables energy estimation in environments where direct physical measurement is unavailable, unlike prior host-dependent approaches.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: growing cloud infrastructure and demand for energy efficiency in virtualized environments.
Potential Customers & Pain Points
- Cloud Service Providers Needing Energy Usage Insights Without Host Access
- Data Center Operators Seeking Cost and Energy Optimization
- Virtualization Platform Developers Lacking Energy Estimation Tools
Business Model
SaaS subscription offering API and dashboard for virtual server energy estimation and analytics to cloud operators and enterprises
Competitive Landscape
- Cloudability
- Datadog
- Turbonomic
Implementation Challenges
- Access to diverse workload data for model generalization
- Integration with existing cloud management platforms
- Convincing customers to trust guest-only energy estimates
Validation Strategy
- Pilot deployment with cloud provider to compare estimates against host measurements
- Benchmark model accuracy across diverse workloads and VM types
- Collect user feedback to refine integration and usability
Research Paper Overview
Data-Driven Energy Estimation for Virtual Servers Using Combined System Metrics and Machine Learning
Summary
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 ≤ R^2 ≤ 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.