Downtime Minimization for Real-time AI Service on Intelligent Edge Nodes: Micro-Renewal Method

Abstract

As the innovation of computing infrastructure evolves to edge computing via cloud computing, intelligent devices such as robots, drones, and autonomous vehicles, which are mobile edge nodes, also surged. Since the edge nodes have limited resources, artificial intelligence services are provided based on lightweight containers. In addition, as intelligent edge node users increase and the categories of users become vast, in order to provide artificial intelligence services according to the situations of all users, data on each situation is collected, and it is necessary to continuously update the learning model. However, if the service is being provided, downtime is inevitable for the updated model to be applied to the service. Therefore, in this paper, we propose a micro-renewal method that minimizes the interruption of the service provided to users in real time when the learning model in the service is updated.