一句话：提出AdaMLS自适应方法，利用模型均衡器与MAPE-K循环，通过轻量无监督学习动态切换ML模型以保障QoS。
机器学习赋能系统（MLS）在生产部署中面临来自模型、软件组件和环境等多方面的运行时不确定性，严重影响服务质量（QoS），而现有自适应技术在MLS中的应用尚待探索。本文提出机器学习模型均衡器（ML Model Balancer）概念，通过多模型管理ML相关不确定性，并引入AdaMLS方法，扩展传统MAPE-K循环实现持续自适应。AdaMLS采用轻量无监督学习进行动态模型切换，以保证一致的QoS。通过自适应目标检测系统原型的初步评估，结果表明AdaMLS在QoS保证上优于朴素方法和单一最先进模型，展示了其在动态环境中实现自适应MLS的潜力。
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 OoS in dynamic environments.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2308-09960,
title = {Towards Self-Adaptive Machine Learning-Enabled Systems Through QoS-Aware Model Switching},
author = {Shubham Kulkarni and Arya Marda and Karthik Vaidhyanathan},
booktitle = {ASE},
year = {2023},
doi = {10.1109/ase56229.2023.00172}
}