5G电力网络切片流量预测及主动调整策略

5G Power Network Slice Traffic Prediction and Proactive Adjustment Strategy

随着5G的推广,网络切片技术也得到了普遍应用,它将物理资源虚拟化,使得网络能够承载多种类型的业务,由此适应于新形势下以及未来电网泛在感知、智能互联式的发展。电力网络中的控制类、采集类、应用类等业务产生了多场景下海量的电力5G切片,也产生了较大的切片优化管理需求。文章在传统的5G切片基础上,结合电网不同业务的特点,提出了利用深度学习进行流量预测的方案,并依据预测结果生成切片资源策略,特别是针对电力5G切片中存在较多的关键性业务切片提出了混合隔离策略。通过实验平台演示和评估,此方法的网络利用效率从传统切片的46.3%提高到71.5%。

With the promotion of 5G,network slicing technology is also widely used.It virtualizes physical resources so that the network can carry multiple types of services,adapting to the new situation and the future development of ubiquitous perception and intelligent interconnection of the power grid.The control,acquisition,and application services in the power network have produced a large number of 5G network slices in multiple scenarios,and have also generated a large number of slice optimization management requirements.Based on the traditional 5G slicing and combining the characteristics of different services in the power grid,this paper proposes a solution for traffic prediction using deep learning,and generates a slicing resource strategy based on the prediction results.In particular,a hybrid isolation strategy is proposed for many key service slices in power 5G slices.Through the demonstration and evaluation of the experimental platform,the network utilization efficiency of this method has increased from 46.3%of the traditional slice to 71.5%.