Use of edge resources for DNN model maintenance in 5G IoT networks

• Published in: Cluster computing Vol. 27; no. 4; pp. 5093 - 5105
• Main Authors: Sung, Jungwoong, Han, Seung-jae
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2024; Springer Nature B.V
• Subjects: 5G mobile communication; Accuracy; Algorithms; Artificial neural networks; Automation; Cloud computing; Computer Communication Networks; Computer Science; Computer vision; Configurations; Controllers; Datasets; Deep learning; Downsizing; Edge computing; Internet of Things; Machine learning; Neural networks; Operating Systems; Processor Architectures; Wireless networks; Deep reinforcement learning; IoT devices; 5G Networks; Light-weight DNN; Continual learning; Edge computing
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-023-04236-y

Internet-of-Things (IoT) services become closely coupled with machine learning and cloud computing, where the 5G network provides the connectivity for the IoT devices. The 5G network can be used not only for connecting the IoT devices to the cloud servers, but also for providing computing resources for ’edge computing’. In this paper, we propose to use the edge node resources of the 5G network for ’inferencing’ and ’training’ the deep neural network (DNN) models for massive IoT services. More specifically, two types of 5G edge nodes are utilized to this end: (i) the ‘IoT controller’, which functions as a 5G-UE (user equipment), (ii) the ‘edge controller’, which is collocated with 5G-UPF (user plane function) in the 5G core network. In the proposed scheme, the downsized DNN models are executed and trained at the IoT controllers. At the edge controller, a deep reinforcement learning (DRL) algorithm is executed to determine the downsizing configuration and the training configuration of the DNN models. The resource constraints of the IoT controllers are considered in these decisions. Extensive evaluations with various DNN models show the effectiveness of the proposed scheme. We show that the proposed scheme achieves proper load balancing even when the resource capacity of individual IoT controllers is very low. For example, fairly complex DNN models for computer vision can be effectively supported by using IoT controllers equipped with the resource capacity of NVIDIA Jetson Nano.