Managing and Deploying Distributed and Deep Neural Models Through Kafka-ML in the Cloud-to-Things Continuum

Abstract

The Internet of Things (IoT) is constantly growing, generating an uninterrupted data stream pipeline to monitor physical world information. Hence, Artificial Intelligence (AI) continuously evolves, improving life quality and business and academic activities. Kafka-ML is an open-source framework that focuses on managing Machine Learning (ML) and AI pipelines through data streams in production scenarios. Consequently, it facilitates Deep Neural Network (DNN) deployments in real-world applications. However, this framework does not consider the distribution of DNN models on the Cloud-to-Things Continuum. Distributed DNN significantly reduces latency, allocating the computational and network load between different infrastructures. In this work, we have extended our Kafka-ML framework to support the management and deployment of Distributed DNN throughout the Cloud-to-Things Continuum. Moreover, we have considered the possibility of including early exits in the Cloud-to-Things layers to provide immediate responses upon predictions. We have evaluated these new features by adapting and deploying the DNN model AlexNet in three different Cloud-to-Things scenarios. Experiments demonstrate that Kafka-ML can significantly improve response time and throughput by distributing DNN models throughout the Cloud-to-Things Continuum, compared to a Cloud-only deployment.

Keywords

• Distributed deep neural networks
• data streams
• cloud computing
• fog/edge computing
• machine learning
• artificial intelligence