基于多维度数据的网络服务质量的综合评估研究

Research on Comprehensive Evaluation of Network Quality of Service Based on Multidimensional Data

随着现代社会经济的迅猛发展,计算机网络被广泛应用到各行各业中,并且发挥着无可替代的重要作用,与此同时,人们对计算机网络服务质量提出了更为明确的要求,如何也网络服务质量一直是互联网领域的研究热点。文中分析了目前网络服务质量综合评估研究的缺陷,同时考虑到传统的数据处理方法在面对数据量庞大、数据类型繁多的情况时缺点会被无限放大,利用稀疏自编码网络模型对多维度数据进行数据降维和特征提取,然后以特征数据集作为实验数据,采用改进灰色关联分析-逼近理想解排序法对网络服务质量进行综合评估,为多层次、多准则综合评估系统提供新的思路。

With the rapid development of modern society and economy,computer network has been widely used in all walks of life,and plays an irreplaceable important role.At the same time,we also puts forward more specific requirements for computer network service quality.How to realize network service quality assurance has always been a hot research topic in the Internet field.This paper analyzes the defects of the current comprehensive evaluation research on network service quality,and at the same time,considers that the shortcomings of traditional data processing methods will be magnified infinitely in the face of huge data volume and various data types.The sparse auto encoder network model is used to conduct data reduction and feature extraction for multidimensional data.Then,the feature data set is taken as experimental data,and the improved grey relational analysis-technique for order preference by similarity to ideal solution model is used to conduct comprehensive evaluation on network service quality.It provides a new way of thinking for multilevel and multicriteria comprehensive evaluation system.