Towards message brokers for generative AI: survey, challenges, and opportunities

Abstract

In today’s digital world, GenAI is becoming increasingly prevalent by enabling unparalleled content generation capabilities for a wide range of advanced applications. This surge in adoption has sparked a significant increase in demand for data-centric GenAI models spanning the distributed edge-cloud continuum, placing increasing demands on communication infrastructures, highlighting the necessity for robust communication solutions. Central to this need are message brokers, which serve as essential channels for data transfer within various system components. This survey aims at delving into a comprehensive analysis of traditional and modern message brokers based on a variety of criteria, highlighting their critical role in enabling efficient data exchange in distributed AI systems. Furthermore, we explore the intrinsic constraints that the design and operation of each message broker might impose, highlighting their impact on real-world applicability. Finally, this study explores the enhancement of message broker mechanisms tailored to GenAI environments. It considers key factors such as scalability, semantic communication, and distributed inference that can guide future innovations and infrastructure advancements in the realm of GenAI data communication.