This paper proposes the neural publish/subscribe paradigm, a novel approach to orchestrating AI workflows in large-scale distributed AI systems in the computing continuum. Traditional centralized broker methodologies are increasingly struggling with managing the data surge resulting from the proliferation of 5G systems, connected devices, and ultra-reliable applications. Moreover, the advent of AI-powered applications, particularly those leveraging advanced neural network architectures, necessitates a new approach to orchestrate and schedule AI processes within the computing continuum. In response, the neural pub/sub paradigm aims to overcome these limitations by efficiently managing training, fine-tuning and inference workflows, improving distributed computation, facilitating dynamic resource allocation, and enhancing system resilience across the computing continuum. We explore this new paradigm through various design patterns, use cases, and discuss open research questions for further exploration.

翻译：本文提出神经发布/订阅范式，这是一种在计算连续体的大规模分布式AI系统中编排AI工作流的新型方法。传统的中心化代理方法在应对5G系统、互联设备及超可靠应用带来的数据激增时日益力不从心。此外，随着AI驱动应用的兴起（特别是那些采用先进神经网络架构的应用），亟需一种在计算连续体内部署和调度AI流程的新方案。为此，神经发布/订阅范式旨在通过高效管理训练、微调与推理工作流，提升分布式计算能力，促进动态资源分配，并增强系统在计算连续体中的弹性，从而突破上述限制。我们通过多种设计模式、用例场景探索这一新范式，并讨论值得进一步研究的开放性问题。