DOI: 10.3724/SP.J.1249.2020.99200
备注
介绍了用大数据分析技术解决校园无线网络优化的问题.通过整合日志和流量两种数据源,对网络设备状态和网络2～7层协议进行全方位的数据采集和分析.这种基于全流量的大数据分析技术克服了过去数据分散而导致分析难的弱点, 为无线网络优化提供了一个新的解决问题的思路.无线接入点(access point, AP)是无线网络的边缘设备, 其布局和通讯质量对无线网络通讯起到了决定性作用,能否通过对其日志和流量的分析得出AP的使用情况是优化的关键.通过并行计算模型,计算出AP的各层协议流量.研究结果将为无线网络布局、设备选型、系统扩容和故障诊断提供更好的解决方案.
This article introduces the use of big data analysis technology to solve the problem of campus wireless network optimization. By integrating two data sources: logs and traffic, a full range of data collection and analysis is carried out on the status of network equipment and the second to seventh layer of the network protocol. The analytics is based on the per-flow big data analysis technology and overcomes the weakness of the past analytics due to the data with scattered nature, and provides a new problem-solving idea for wireless network optimization. The wireless access point(AP)is the edge device of the wireless network. Its layout and communication quality play a decisive role in wireless network communication. Whether the effective usage of AP can be obtained by analyzing its log and traffic is the key to optimization. This research focuses on calculating the network traffic of each protocol layer of AP through a parallel computing model. The research results will provide better solutions for wireless network layout, equipment selection, system expansion, and fault diagnosis.
