[1]盖 璇.基于云计算和分布式技术的流量分析模型[J].计算机技术与发展,2022,32(S2):114-119.[doi:10. 3969 / j. issn. 1673-629X. 2022. S2. 020]
　GAI Xuan.Traffic Analysis Model Based on Cloud Computing and Distributed Technology[J].,2022,32(S2):114-119.[doi:10. 3969 / j. issn. 1673-629X. 2022. S2. 020]
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基于云计算和分布式技术的流量分析模型()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年S2期
页码:
114-119
栏目:
网络有安全
出版日期:
2022-12-11

文章信息/Info

Title:
Traffic Analysis Model Based on Cloud Computing and Distributed Technology
文章编号:
1673-629X(2022)S2-0114-06
作者:
盖 璇
东北石油大学,黑龙江 大庆 163318
Author(s):
GAI Xuan
Northeast Petroleum University,Daqing 163318,China
关键词:
云计算; 分布式处理技术; 网络流量; 海量网络数据; 流量控制
Keywords:
cloud computing; distributed processing technology; network flow; massive network data; flow schedule
分类号:
TP393
DOI:
10. 3969 / j. issn. 1673-629X. 2022. S2. 020
摘要:
传统海量网络流量分析模型采用串行分析方式,在运行中存在时间开销大的问题,为此提出基于云计算和分布式处理技术的海量网络流量分析模型。 首先结合云计算与分布式处理技术的运行方式,搭建模型结构,在该结构下实时采集网络中的流量数据,并作为模型的输入值。 通过对初始数据的存储、分类以及异常检测等处理,分别得出海量网络流量的分析结果,综合多个方面的分析结果得出模型的输出项。 实验对比结果表明,通过云计算和分布式处理技术的应用有效地降低少量用户在线环境和大量用户在线环境时间开销,在分析速度上具有明显优势。
Abstract:
The traditional massive network traffic analysis model adopts the serial analysis method,which has the problem of large timecost in operation. Therefore,a? ? ?massive network traffic analysis model based on cloud computing and distributed processing technology isproposed. Firstly,combining the operation mode of? ?cloud computing and distributed processing technology,a model structure is built,under which the traffic data in the network is collected in real time and used? ? ? ? ?as the input value of the model. Through the initial datastorage,classification and exception detection, the analysis results of massive network traffic are obtained respectively, and the outputitems of the model are obtained by synthesizing the analysis results of many aspects. The experimental results show that the application ofcloud computing and distributed processing technology can effectively reduce the time cost of a small number of users’ online environmentand a large number of users’ online environment,and has obvious advantages in the analysis speed.

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更新日期/Last Update: 2022-10-10