A collaborative cloud-edge computing framework in distributed neural network

• Main Authors: Shihao Xu, Zhenjiang Zhang, Michel Kadoch, Mohamed Cheriet
• Format: Article
• Language: English
• Published: SpringerOpen 2020-10-01
• Series: EURASIP Journal on Wireless Communications and Networking
• Subjects: Edge computing; Distributed neural network; Resource allocation; Task offloading
• Online Access: http://link.springer.com/article/10.1186/s13638-020-01794-2

Abstract The emergence of edge computing provides a new solution to big data processing in the Internet of Things (IoT) environment. By combining edge computing with deep neural network, it can make better use of the advantages of multi-layer architecture of the network. However, the current task offloading and scheduling frameworks for edge computing are not well applicable to neural network training tasks. In this paper, we propose a task model offloading algorithm by considering how to optimally deploy neural network model into the edge nodes. An adaptive task scheduling algorithm is also designed to adaptively optimize the task assignment by using the improved ant colony algorithm. Based on them, a collaborative cloud-edge computing framework is proposed, which can be used in the distributed neural network. Moreover, this framework sets up some mechanisms so that the cloud can collaborate with edge computing in the work. The simulation results show that the framework can reduce time delay and energy consumption, and improve task accuracy.