LiquidAI : Towards an Isomorphic AI/ML System Architecture for the Cloud-Edge Continuum

Abstract

A typical Internet of Things (IoT) system consists of a large number of different subsystems and devices, including sensors and actuators, gateways that connect them to the Internet, cloud services, end-user applications and analytics. Today, these subsystems are implemented with a broad variety of programming technologies and tools, making it difficult to migrate functionality from one subsystem to another. In our earlier papers, we have predicted the rise of isomorphic IoT system architectures in which all the subsystems can be developed with a consistent set of technologies. In this paper we expand the same research theme to machine learning technologies, highlighting the need to use ML in a consistent and uniform fashion across the entire Cloud-Edge continuum.