基于云计算和大数据分析的大规模网络流量预测  

Large scale network traffic prediction based on cloud computing and big data analysis

作　　者：李晓会[1] 陈潮阳 伊华伟[1] 李波[1] LI Xiao-hui;CHEN Chao-yang;YI Hua-wei;LI Bo(School of Electronics and Information Engineering,Liaoning University of Technology,Jinzhou 121001,China)

机构地区：[1]辽宁工业大学电子与信息工程学院,辽宁锦州121001

出　　处：《吉林大学学报（工学版）》2021年第3期1034-1039,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金青年基金项目(61802161);辽宁省教育厅科学研究项目(JZL202015402);国家自然科学基金项目(51679116);辽宁省科技厅基金项目(20180550886)。

摘　　要：结合当前网络流量海量、混沌、随机性变化的特点,为了获得理想的网络流量预测结果,提出了基于云计算和大数据分析的大规模网络流量预测模型。首先,根据混沌算法描述网络流量的混沌变化特点,建立学习样本集合。然后,引入支持向量机对网络流量随机性变化特点进行建模,并结合历史数据海量的特点,采用云计算平台使多个支持向量机并行运行。对比测试结果表明,该模型提升了网络流量预测精度,建模效率大幅度提升,可以满足网络流量在线管理的实时性要求。In order to obtain the ideal network traffic prediction results,a large-scale network traffic prediction model based on cloud computing and big data analysis is proposed.Firstly,according to the chaos algorithm to describe chaos characteristics of network traffic,the learning sample set is established.Then the support vector machine is introduced to model the randomness characteristics of network traffic,and combined with the massive characteristics of historical data,the cloud computing platform is used to make multiple support vector machines run in parallel.Finally,the comparative test results show that the proposed model improves the accuracy of network traffic prediction,and the modeling efficiency is greatly improved,which can meet the real-time requirements of online network traffic management.

关 键 词：网络管理 云计算平台 并行建模 随机性 学习样本 混沌算法 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]