基于ARMA模型的网络流量预测

随着计算机网络的迅速发展,目前的网络规模极为庞大和复杂,因此发生各种问题的可能性也越大,同时管理网络的难度也增大,传统的网络管理是在告警之后,解决潜在的问题,即为一种响应式的行为,这时候网络的服务很可能已经受到影响,根据实际采集的非单播包数的观测值序列,建立该流量参数的正常行为,然后平稳化该序列,估计出网络流量的ARMA（2,1）模型,用线性最小均方误差预测方法,对网络流量进行预测,并检测在将来超越阈值的可能性和发生时间,这样,在网络过载发生之前,可以预先采取防范措施,来保证网络的正常服务,这种方法改变了以往的网络管理响应方式,使得网络过载的预警成为可能。

ARMA-BASED TRAFFIC PREDICTION AND OVERLOAD DETECTION OF NETWORK

With its rapid development the network now has a large size and complexity, and consequently the network management is becoming increasingly difficult. Generally the network manager begins to solve the potential problems after the monitoring system alarms, i.e., it takes a re action way, so the service on the network is possibly affected. A normal behavior model is founded from the numbers of the non unicasting packet collected from a real network. The normal behavior serial is stabilized and the coefficients of the ARMA model are estimated. And then the traffic by the way of "minimal linear square error" is predicted, and the probability of the predicted value exceeding the threshold is calculated. So the overload in the network may be predicted, and the recovery measures may be taken beforehand to prevent communication from being impacted or to reduce its severity. This method changes the traditional network management from re action to prediction beforehand, so that the network overload may be predicted.