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Measuring the impact of virtualization and containerization on the environment when using GPUs for processing the AI

Safaa Hriez¹, Mohammad Haikal²

¹Cyber Security Department, Al Hussein Technical University, Amman, Jordan.

Frontiers in Big Data

|July 1, 2026

Summary

This summary is machine-generated.

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

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Containerization is more energy-efficient than virtualization for AI workloads. This approach reduces energy consumption and carbon dioxide emissions by approximately 21.6% compared to virtual machines.

Area of Science:

Computer Science
Artificial Intelligence
Sustainable Computing

Background:

Artificial intelligence (AI) growth drives high computing demand.
Virtualization and containerization optimize resources but their energy efficiency for AI workloads is unclear.

Purpose of the Study:

To compare the energy efficiency and environmental impact of containerization versus virtualization for GPU-accelerated AI workloads.
To quantify differences in power consumption and resource utilization.

Main Methods:

Trained a DenseNet-121 model on MNIST using VirtualBox and Docker.
Measured performance, GPU utilization, and power consumption for both environments.

Main Results:

Containerization (Docker) reduced energy consumption by ~21.6% compared to virtualization (VirtualBox).

Keywords:

AI workloads Graphics Processing Unit (GPU)artificial intelligence (AI)computer vision containerization energy consumption energy efficiency environmental impact

Related Experiment Videos

Containerization showed lower average and peak GPU utilization and power draw.

Significant reductions in carbon dioxide emissions were observed with containerization.

Conclusions:

Containerization is a more energy-efficient and sustainable choice for GPU-enabled AI workloads.
Results suggest potential for reduced environmental impact without performance loss.
Further research is needed for diverse AI models and hardware.