Energy-Aware Node Selection for Cloud-Based Parallel Workloads with Machine Learning and Infrastructure as Code

Denis B. Citadin, Fábio Rossi, Marcelo Luizelli, Philippe Navaux, Arthur Lorenzon

2025

Abstract

Cloud computing has become essential for executing high-performance computing (HPC) workloads due to its on-demand resource provisioning and customization advantages. However, energy efficiency challenges persist, as performance gains from thread-level parallelism (TLP) often come with increased energy consumption. To address the challenging task of optimizing the balance between performance and energy consumption, we propose SmartNodeTuner. It is a framework that leverages artificial intelligence and Infrastructure as Code (Iac) to optimize performance-energy trade-offs in cloud environments and provide seamless infrastructure management. SmartNodeTuner is split into two main modules: a BuiltModel Engine leveraging an artificial neural network (ANN) model trained to predict optimal TLP and node configurations; and AutoDeploy Engine using IaC with Terraform to automate the deployment and resource allocation, reducing manual efforts and ensuring efficient infrastructure management. Using ten well-known parallel workloads, we validate SmartNodeTuner on a private cloud cluster with diverse architectures. It achieves a 38.2% improvement in the Energy-Delay Product (EDP) compared to Kubernetes’ default scheduler and consistently predicts near-optimal configurations. Our results also demonstrate significant energy savings with negligible performance degradation, highlighting SmartNodeTuner ’s effectiveness in optimizing resource use in heterogeneous cloud environments.

Paper Citation

in Harvard Style

Citadin D., Rossi F., Luizelli M., Navaux P. and Lorenzon A. (2025). Energy-Aware Node Selection for Cloud-Based Parallel Workloads with Machine Learning and Infrastructure as Code. In Proceedings of the 15th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER; ISBN 978-989-758-747-4, SciTePress, pages 49-60. DOI: 10.5220/0013418500003950

in Bibtex Style

@conference{closer25,
author={Denis Citadin and Fábio Rossi and Marcelo Luizelli and Philippe Navaux and Arthur Lorenzon},
title={Energy-Aware Node Selection for Cloud-Based Parallel Workloads with Machine Learning and Infrastructure as Code},
booktitle={Proceedings of the 15th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER},
year={2025},
pages={49-60},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013418500003950},
isbn={978-989-758-747-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER
TI - Energy-Aware Node Selection for Cloud-Based Parallel Workloads with Machine Learning and Infrastructure as Code
SN - 978-989-758-747-4
AU - Citadin D.
AU - Rossi F.
AU - Luizelli M.
AU - Navaux P.
AU - Lorenzon A.
PY - 2025
SP - 49
EP - 60
DO - 10.5220/0013418500003950
PB - SciTePress