# EdgeMLBalancer: A Self-Adaptive Approach for Dynamic Model Switching on Resource-Constrained Edge Devices

arXiv: https://arxiv.org/abs/2502.06493 · HTML: https://mlsanity.com/paper/2502.06493/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2025.

## Built on by (most cited first, 5 of 5)

- [Choosing to Be Green: Advancing Green AI via Dynamic Model Selection](https://mlsanity.com/paper/2509.19996/) (2025)
- [ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge](https://mlsanity.com/paper/2507.06011/) (2025)
- [Harmonica: A Self-Adaptation Exemplar for Sustainable MLOps](https://mlsanity.com/paper/2601.11926/) (2026)
- [Breaking the accuracy-resource dilemma: a lightweight adaptive video inference enhancement](https://mlsanity.com/paper/2601.14568/) (2026)
- [Toward a Sustainable Software Architecture Community: Evaluating ICSA's Environmental Impact](https://mlsanity.com/paper/2604.04096/) (2026)

## Abstract (arXiv)

The widespread adoption of machine learning on edge devices, such as mobile phones, laptops, IoT devices, etc., has enabled real-time AI applications in resource-constrained environments. Existing solutions for managing computational resources often focus narrowly on accuracy or energy efficiency, failing to adapt dynamically to varying workloads. Furthermore, the existing system lack robust mechanisms to adaptively balance CPU utilization, leading to inefficiencies in resource-constrained scenarios like real-time traffic monitoring. To address these limitations, we propose a self-adaptive approach that optimizes CPU utilization and resource management on edge devices. Our approach, EdgeMLBalancer balances between models through dynamic switching, guided by real-time CPU usage monitoring across processor cores. Tested on real-time traffic data, the approach adapts object detection models based on CPU usage, ensuring efficient resource utilization. The approach leverages epsilon-greedy strategy which promotes fairness and prevents resource starvation, maintaining system robustness. The results of our evaluation demonstrate significant improvements by balancing computational efficiency and accuracy, highlighting the approach's ability to adapt seamlessly to varying workloads. This work lays the groundwork for further advancements in self-adaptation for resource-constrained environments.