发展基于大数据的网络流量研究与分析模型

Mahesh Pawar, A. Panday, Ratish Agrawal, Sachin Goyal
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引用次数: 1

摘要

网络是以有线或无线方式连接设备的一种方式。网络已经成为当今世界计算的重要组成部分。它们构成了现代计算机行业的支柱。因此,网络始终保持活跃、正常和可靠是非常重要的。确保这一点的一种方法是网络流量分析。网络流量分析主要研究带宽利用率、收发率、错误率等,对保持网络畅通、提高经济效益具有重要意义。该模型通过收集网络信息,然后使用可用的大数据分析技术来处理网络流量分析。该模型旨在对收集到的信息进行分析,计算出一个称为可靠性因子的因子,从而指导有效的网络管理。该模型还旨在通过通知网络流量是高还是低来帮助网络管理员,然后管理员可以采取有针对性的措施来防止网络故障。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Developing a Big-Data-Based Model to Study and Analyze Network Traffic
Network is a connection of devices in either a wired or wireless manner. Networking has become a part and parcel of computing in the present world. They form the backbone of the modern-day computing business. Hence, it is important for networks to remain alive, up, and reliable all the time. A way to ensure that is network traffic analysis. Network traffic analysis mainly deals with a study of bandwidth utilization, transmission and reception rates, error rates, etc., which is important to keep the network smooth and improve economic efficiency. The proposed model approaches network traffic analysis in a way to collect network information and then deal with it using technologies available for big data analysis. The model aims to analyze the collected information to calculate a factor called reliability factor, which can guide in effective network management. The model also aims to assist the network administrator by informing him whether network traffic is high or low, and the administrator can then take targeted steps to prevent network failure.
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