Effective ML Model Versioning in Edge Networks
This addresses the challenge of managing ML updates in resource-constrained edge environments, which is an incremental improvement for edge computing systems.
The paper tackles the problem of automating ML model version updates in edge networks, formulating it as an optimization problem for the first time and proposing a reinforcement learning-based solution that improves security, reliability, and/or model accuracy while maintaining lower response times across server load ranges.
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 update automation with reinforcement learning (RL) based algorithm. We study the edge network environment due to the known constraints in performance, response time, security, and reliability, which make updates especially challenging. The performance study shows that model version updates can be fully and effectively automated with reinforcement learning method. We show that for every range of server load values, the proper versioning can be found that improves security, reliability and/or ML model accuracy, while assuring a comparably lower response time.