Machine learning (ML) models, data and software need to be regularly updated whenever essential version updates are released and feasible for integration. This is a basic but most challenging requirement to satisfy in the edge, due to the various system constraints and the major impact that an update can have on robustness and stability. In this paper, we formulate for the first time the ML model versioning optimization problem, and propose effective solutions, including the automation with reinforcement learning (RL) based algorithm. Without loss of generality, we choose the edge network environment due to the known constraints in performance, response time, security, and reliability. The performance study shows that ML model version updates can be fully and effectively automated with reinforcement learning method as compared to other approaches. We show that with a carefully chosen range of traffic load values, the proper versioning can improve the security, reliability and ML model accuracy, while assuring a comparably lower response time.

翻译：机器学习（ML）模型、数据及软件需在关键版本更新发布且具备集成可行性时定期更新。由于边缘系统存在多种约束条件，且更新可能对系统鲁棒性与稳定性产生重大影响，满足这一基础要求成为边缘计算中最具挑战性的任务之一。本文首次构建了机器学习模型版本管理的优化问题，并提出包括基于强化学习（RL）算法的自动化解决方案在内的有效方法。在不失一般性的前提下，我们选择以边缘网络环境为研究对象，因其在性能、响应时间、安全性和可靠性方面存在已知约束。性能研究表明，相较于其他方法，强化学习方法能够全面有效地实现机器学习模型版本更新的自动化。我们证明，通过精心选择流量负载值的范围，恰当的版本管理可在确保较低响应时间的同时，提升安全性、可靠性及机器学习模型的准确性。