The current dependency of Artificial Intelligence (AI) systems on Cloud computing implies higher transmission latency and bandwidth consumption. Moreover, it challenges the real-time monitoring of physical objects, e.g., the Internet of Things (IoT). Edge systems bring computing closer to end devices and support time-sensitive applications. However, Edge systems struggle with state-of-the-art Deep Neural Networks (DNN) due to computational resource limitations. This paper proposes a technology framework that combines the Edge-Cloud architecture concept with BranchyNet advantages to support fault-tolerant and low-latency AI predictions. The implementation and evaluation of this framework allow assessing the benefits of running Distributed DNN (DDNN) in the Cloud-to-Things continuum. Compared to a Cloud-only deployment, the results obtained show an improvement of 45.34% in the response time. Furthermore, this proposal presents an extension for Kafka-ML that reduces rigidness over the Cloud-to-Things continuum managing and deploying DDNN.

当前人工智能 (AI) 系统对云计算的依赖意味着更高的传输延迟和带宽消耗。此外，它挑战了物理对象的实时监控，例如物联网 (IoT)。边缘系统使计算更接近终端设备并支持时间敏感的应用程序。然而，由于计算资源的限制，边缘系统与最先进的深度神经网络 (DNN) 斗争。本文提出了一种技术框架，将 Edge-Cloud 架构概念与 BranchyNet 优势相结合，以支持容错和低延迟的 AI 预测。该框架的实施和评估允许评估在 Cloud-to-Things 连续体中运行分布式 DNN (DDNN) 的好处。与仅云部署相比，获得的结果显示响应时间提高了 45.34%。此外，该提案提出了 Kafka-ML 的扩展，可降低 Cloud-to-Things 连续体管理和部署 DDNN 的刚性。