Adaptive network flow forecasting and abnormal alarming method

Abstract

This invention provide a network flow performance prediction and abnormal alarming technology and a method based on the genetic algorithms, which utilizes genetic algorithms to predict the flow state of the next time from the horizontal time degree then to utilize the statistics method to judge the abnormal situation of said flow based on the flow sample in a related time period of each day in the historical flow information from the longitudinal time degree. The method is realized by four steps of network flow data collection, process and storage, prediction and abnormal alarming.

Description

technical field [0001] The invention relates to the technical field of computer network management, in particular to a self-adaptive network flow prediction and abnormal alarm method. Background technique [0002] With the rapid development of network technology and continuous expansion of scale, the complexity of network services is increasing day by day. In this environment, the degree of resource distribution and sharing is rapidly expanding, and any tiny failure may cause the failure of user applications. In order to improve service quality and reduce operating costs, it is becoming more and more important to timely and effectively discover abnormalities in network traffic in network management. Prediction and warning are two effective means. [0003] Forecasting the time series of network traffic, the classic method is the statistical model method based on the Poisson process. In the current environment where new network services are constantly emerging, the traffic ...

AI Technical Summary

Problems solved by technology

In the actual network environment, it is very difficult to set the threshold. If the threshold is too large, the missed detection rate will increase, and if the threshold is too small, the false detection rate will increase.

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