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

• URL: http://arxiv.org/abs/2308.09960v1
• Date: Sat, 19 Aug 2023 09:33:51 GMT
• Title: Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching
• Authors: Shubham Kulkarni, Arya Marda, Karthik Vaidhyanathan
• Abstract summary: We propose the concept of a Machine Learning Model Balancer, focusing on managing uncertainties related to ML models by using multiple models. AdaMLS is a novel self-adaptation approach that leverages this concept and extends the traditional MAPE-K loop for continuous MLS adaptation. Preliminary results suggest AdaMLS surpasses naive and single state-of-the-art models in guarantees.
• Score: 1.2277343096128712
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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