  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.
