Abstract: ith the deepening of 5G research and the advancement of commercialization,it also brings various challenges.Among them,the resource management of 5G communication system is a key challenge for the research of 5G network.Network Function Virtualization (NFV) technology provides key support for 5G implementation,and it also introduces new research directions for 5G resource management issues.However,resource management in network function virtualization scenarios is a more complex issue.In particular,different placements of virtual network functions have different effects on their performance.Firstly,this paper analyzed and studied the impact of NFV resource allocation methods and NFV placement on performance.On this basis,this paper mainly discussed machine learning based on the example proposed by Knowledge Definition Network (KDN).The technology is applied to the study of virtual network function memory resource management,constructing a neural network learning model,and predicting memory resource consumption.Secondly,the focus of this paper is on the extraction of the characteristics of the input traffic.The traffic is mainly represented by a set of features,which represent small batches of information from the data link layer to the transport layer,where the memory consumption is from the hypervisor.The average memory consumption of the batch is obtained on the performance monitoring tool.Finally,this paper aimed to manage memory resources by using neural networks to predict memory resource consumption.

Key words: 5G Communication network, Machine learning, Memory consumption, Network function virtualization, Resource management