An SDN traffic prediction method based on RBF neural network

Abstract

The invention discloses an SDN flow prediction method based on RBF neural network, belonging to the technical field of wireless communication network. The SDN traffic prediction algorithm based on theRBF neural network provided by the invention has excellent non-linear characteristics, is particularly suitable for processing highly non-linear system, and is trained by studying historical data records, so that a properly trained neural network has the ability of inducing all data. Secondly, the RBF neural network has a flexible and effective way of learning. Compared with other neural networks, the structure of RBF neural network is simpler and the learning speed is faster. Therefore, the RBF neural network can predict the complex and changeable network traffic more accurately. The invention utilizes POX and Mininet to simulate the proposed algorithm, and the simulation results show that the proposed algorithm can accurately predict the SDN traffic change trend, and has better prediction performance and lower prediction error.

Description

technical field [0001] The invention belongs to the technical field of wireless communication networks, and in particular relates to a performance analysis and network planning method suitable for networks. Background technique [0002] Traffic prediction is of great significance for network performance analysis and network planning. In the traditional TCP / IP network, the distributed network architecture makes the network less flexible and intelligent, which leads to the fact that the traffic prediction algorithm cannot be well applied in the industry. As a new type of network architecture, Software Defined Networking (SDN) has the characteristics of decoupling and separation of control plane and data plane, open programmable interface and logical centralized control, making SDN flexible and intelligent Compared with the traditional network, it has been greatly improved. Therefore, the proposal of SDN provides a good platform for the application of traffic prediction algor...

AI Technical Summary

Problems solved by technology

In the second stage, due to the limitation of the traditional model, which only has short correlation, it cannot describe the long correlation of network traffic

In the third stage, the network scale is increasing and the parameter calculation of the self-similar model is too complicated, which leads to the decline of the prediction performance of the self-similar model. Therefore, a prediction method based on intelligent algorithm is proposed

In the traditional TCP / IP network, the distributed network architecture makes the network less flexible and intelligent, resulting in poor application of traffic prediction

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