Title: Multi-objective optimisation of traffic data transmission load based on improved machine learning in the edge computing framework

Authors: Zhichang Huang

Addresses: College of Artificial Intelligence and Software, Nanning University, Nanning, Guangxi, China

Abstract: To solve the problems of high packet loss rate, high delay and low load balance in the traditional method, a multi-objective optimisation method of traffic data transmission load based on improved machine learning in the edge computing framework is proposed. In the edge computing framework, traffic data is collected and traffic data transmission path is selected. The multi-objective optimisation function of traffic data transmission load is built, and the traffic data transmission load scheduling model is built by combining the multi-objective optimisation function and the improved support vector machine. The traffic data transmission path and traffic data are input into the model, and the relevant optimisation results are obtained. The experimental results show that the maximum packet loss rate of traffic data transmission is 6.28%, the maximum delay of traffic data transmission is 57.91 ms, and the load balance of traffic data transmission is between 0.96 and 0.98.

Keywords: edge computing framework; improve machine learning; traffic data; transmission load; multi-objective optimisation; improved support vector machine.

DOI: 10.1504/IJCAT.2024.143292

International Journal of Computer Applications in Technology, 2024 Vol.74 No.4, pp.316 - 323

Received: 02 Apr 2024
Accepted: 08 Jul 2024
Published online: 12 Dec 2024 *

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