# Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching

arXiv: https://arxiv.org/abs/2308.09960 · HTML: https://mlsanity.com/paper/2308.09960/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 19 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [Interactron: Embodied Adaptive Object Detection](https://mlsanity.com/paper/2202.00660/) (2022)
- [Self-Guided Adaptation: Progressive Representation Alignment for Domain Adaptive Object Detection](https://mlsanity.com/paper/2003.08777/) (2020)
- [Software Architecture for ML-based Systems: What Exists and What Lies Ahead](https://mlsanity.com/paper/2103.07950/) (2021)
- [Self-Adaptive Systems: A Systematic Literature Review Across Categories and Domains](https://mlsanity.com/paper/2101.00125/) (2021)

## Built on by (most cited first, 7 of 7)

- [The Vision of Autonomic Computing: Can LLMs Make It a Reality?](https://mlsanity.com/paper/2407.14402/) (2024)
- [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)
- [ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge](https://mlsanity.com/paper/2507.06011/) (2025)
- [POLARIS: Is Multi-Agentic Reasoning the Next Wave in Engineering Self-Adaptive Systems?](https://mlsanity.com/paper/2512.04702/) (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)

## Abstract (arXiv)

Machine Learning (ML), particularly deep learning, has seen vast advancements, leading to the rise of Machine Learning-Enabled Systems (MLS). However, numerous software engineering challenges persist in propelling these MLS into production, largely due to various run-time uncertainties that impact the overall Quality of Service (QoS). These uncertainties emanate from ML models, software components, and environmental factors. Self-adaptation techniques present potential in managing run-time uncertainties, but their application in MLS remains largely unexplored. As a solution, we propose the concept of a Machine Learning Model Balancer, focusing on managing uncertainties related to ML models by using multiple models. Subsequently, we introduce AdaMLS, a novel self-adaptation approach that leverages this concept and extends the traditional MAPE-K loop for continuous MLS adaptation. AdaMLS employs lightweight unsupervised learning for dynamic model switching, thereby ensuring consistent QoS. Through a self-adaptive object detection system prototype, we demonstrate AdaMLS's effectiveness in balancing system and model performance. Preliminary results suggest AdaMLS surpasses naive and single state-of-the-art models in QoS guarantees, heralding the advancement towards self-adaptive MLS with optimal QoS in dynamic environments.