Network traffic base line self-learning adaptive method

Abstract

The invention provides a network traffic base line self-learning adaptive method, comprising the steps of collecting and storing network traffic data, calculating network traffic base line values, determining a dynamic base line critical value and updating the base line values and the critical value. According to the technical scheme provided by the invention, when a service traffic fixed threshold is not set, important traffic anomaly alarm information can be provided for a maintainer, a network manager is effectively assisted in sensing and discovering an anomaly in the network as soon as possible, and a network traffic data trend can be visually reflected.

Description

technical field [0001] The invention relates to a network flow monitoring method, in particular to a network flow baseline self-learning self-adaptive method. Background technique [0002] A very important and basic link in network management is network traffic monitoring, which is to monitor network traffic through continuous collection of network data. Network administrators can perform performance management on the performance of the network and its main components based on the current and historical data stored on the performance of the network and its important components, and obtain performance change trends through data analysis. [0003] On the basis of network traffic monitoring, administrators can set threshold ranges for interested network management objects to configure network threshold objects. Threshold object monitoring polls the network in real time to obtain the current value of defined objects. If the upper and lower limits of the threshold are exceeded, ...

AI Technical Summary

Problems solved by technology

[0007] In order to meet the needs of the development of existing technologies and overcome the shortcomings of inaccurate traffic monitoring in the prior art, the present invention provides a network traffic baseline self-learning adaptive method

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