Research And Realization Of Network Flow Predicting System

At present, the features of network behavior are becoming increasing complicated, which, with the rapid development of Internet, brings a great challenge to the network planning and network management. One of the present hot points is to create an effective network flow model for the network flow analysis. The network flow model is a depiction of the features of the network flow. A good model not only should accurately reflect the features of historic flow but also can predict the size of the network flow in a time to come. Some traditional models usually assume that the network flow meets with the linear relationship and then describe the system with the linear recursion and combination. In fact, the network flow data don't show the remarkable law with the change of the time, because they contain many non-linear factors. This paper, around the self-adapting ability and increasing the accuracy of prediction, makes a detailed analysis with the non-linear model in the network flow prediction, and designs and realizes an analyzing system of network flow prediction on that basis.The network flow prediction system introduced in this paper, on the basis of introducing the theory of time series prediction and the theory of artificial neural network, designed a flow predicting model based on the BP-neural network, offering an effective predicting solution. The dynamic call predicting function in the predicting model improves the flexibility and efficiency of the system introduced in this paper, which is also of a stronger self-adapting ability.This system acquires a best predicting model through much experiment; by means of this system, an effective prediction can be made on the network flow in the short-term period and on the information, which offers an active and beneficial exploration in the network planning and network management.