Managing and Deploying Distributed and Deep Neural Models Through Kafka-ML in the Cloud-to-Things Continuum
| Autor: | Cristian Martín, Daniel R. Torres, Alejandro Carnero, Bartolomé Rubio, Daniel Garrido, Manuel Díaz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Data stream
General Computer Science Artificial neural network Distributed database business.industry Computer science Data stream mining Deep learning Distributed computing cloud computing General Engineering data streams Cloud computing Throughput Distributed deep neural networks artificial intelligence Pipeline (software) TK1-9971 machine learning General Materials Science fog/edge computing Artificial intelligence Electrical engineering. Electronics. Nuclear engineering business |
| Zdroj: | IEEE Access, Vol 9, Pp 125478-125495 (2021) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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