Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching
This addresses software engineering challenges for deploying MLS in production by enabling self-adaptation to dynamic environments, though it appears incremental as it builds on existing self-adaptation techniques.
The paper tackles the problem of managing run-time uncertainties in Machine Learning-Enabled Systems (MLS) by proposing AdaMLS, a self-adaptation approach that uses dynamic model switching to ensure consistent Quality of Service (QoS), with preliminary results showing it surpasses naive and single state-of-the-art models in QoS guarantees.
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.