基于特征权重感知的VNF资源需求预测方法  

Feature Weight Perception-based Prediction of Virtual Network Function Resource Demands

作　　者：王怀芹 骆健[1,2] 王海艳[1,2] WANG Huaiqin;LUO Jian;WANG Haiyan(School of Computer Science,Nanjing University of Post and Telecommunications,Nanjing 210023,China;Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,Nanjing University of Post and Telecommunications,Nanjing 210023,China)

机构地区：[1]南京邮电大学计算机学院,南京210023 [2]南京邮电大学江苏省大数据安全与智能处理重点实验室,南京210023

出　　处：《计算机科学》2023年第9期331-336,共6页Computer Science

基　　金：国家自然科学基金(62272243)。

摘　　要：虚拟网络功能(Virtual Network Function,VNF)以服务功能链(Service Function Chain,SFC)的形式提供服务,能够满足不同服务的性能需求。由于网络具有动态性,为VNF实例分配固定资源会导致VNF实例的资源过多或者不足的问题。以往的研究对于VNF配置文件相关网络负载特征的重要性未做区分,因此,提出了一种基于特征权重感知的动态VNF资源需求预测方法。首先,使用ECANet学习VNF特征的权重值,以此来减少无用特征对模型预测结果的消极影响。其次,由于VNF配置文件数据集具有结构化特性,构建VNF资源预测模型时需要考虑以加强特征交互的方式来挖掘特征间深层的相互关系,提出使用深度特征交互网络(Deep Feature-Interactive Network,DIN)增强网络负载特征与VNF性能特征之间的交互能力,提高模型预测精度。最后,在基准数据集上将所提方法与同类方法进行对比实验,发现其在预测的有效性与精确性上更具优势。Virtual network function(VNF)provides services in the form of service function chain(SFC)to meet the performance requirements of different services.Due to the dynamic nature of the network,allocating fixed resources to VNF instances will lead to excessive or insufficient resources for VNF instances.Previous studies have not distinguished the importance of network load characteristics related to VNF profiles.Therefore,a dynamic VNF resource demand prediction method based on feature weight perception is proposed.Firstly,ECANet is used to learn the weight values of VNF features,to reduce the negative impact of useless features on the model prediction results.Secondly,because the VNF profile data set has structural characteristics,when building the VNF resource prediction model,it is necessary to consider mining the deep interrelationship between features by strengthening feature interaction.It is proposed to use the deep feature interactive network(DIN)to enhance the interaction between network load features and VNF performance features,so as to improve the prediction accuracy of the model.Finally,compared with similar methods on the benchmark dataset,it is found that the proposed method has more advantages in the effectiveness and accuracy of prediction.

关 键 词：资源预测 服务功能链 虚拟网络功能 高效通道注意力网络 特征交互 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]