# EcoMLS: A Self-Adaptation Approach for Architecting Green ML-Enabled Systems

arXiv: https://arxiv.org/abs/2404.11411 · HTML: https://mlsanity.com/paper/2404.11411/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2024.

## Built on by (most cited first, 3 of 3)

- [Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices](https://mlsanity.com/paper/2506.01774/) (2025)
- [All Required, In Order: Phase-Level Evaluation for AI-Human Dialogue in Healthcare and Beyond](https://mlsanity.com/paper/2601.08690/) (2026)
- [HarmonE: A Self-Adaptive Approach to Architecting Sustainable MLOps](https://mlsanity.com/paper/2505.13693/) (2025)

## Abstract (arXiv)

The sustainability of Machine Learning-Enabled Systems (MLS), particularly with regard to energy efficiency, is an important challenge in their development and deployment. Self-adaptation techniques, recognized for their potential in energy savings within software systems, have yet to be extensively explored in Machine Learning-Enabled Systems (MLS), where runtime uncertainties can significantly impact model performance and energy consumption. This variability, alongside the fluctuating energy demands of ML models during operation, necessitates a dynamic approach. Addressing these challenges, we introduce EcoMLS approach, which leverages the Machine Learning Model Balancer concept to enhance the sustainability of MLS through runtime ML model switching. By adapting to monitored runtime conditions, EcoMLS optimally balances energy consumption with model confidence, demonstrating a significant advancement towards sustainable, energy-efficient machine learning solutions. Through an object detection exemplar, we illustrate the application of EcoMLS, showcasing its ability to reduce energy consumption while maintaining high model accuracy throughout its use. This research underscores the feasibility of enhancing MLS sustainability through intelligent runtime adaptations, contributing a valuable perspective to the ongoing discourse on energy-efficient machine learning.