An SDN traffic prediction method based on RBF neural network

A neural network and traffic prediction technology, applied in biological neural network models, prediction, data processing applications, etc., can solve the problem of low network flexibility and intelligence, inability to describe the long correlation of network traffic, and poor traffic prediction. applications, etc.

Inactive Publication Date: 2019-02-12
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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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 th...

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  • An SDN traffic prediction method based on RBF neural network
  • An SDN traffic prediction method based on RBF neural network
  • An SDN traffic prediction method based on RBF neural network

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[0070] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0071] A SDN traffic prediction algorithm based on RBF neural network, the specific steps are as follows:

[0072] Step 1: SDN traffic measurement and sampling

[0073] The specific process of the equal time interval sampling algorithm is as follows:

[0074] First select the network flow to be measured, and then determine the network path through which the measured network flow passes, thereby determining the switch through which the network flow passes. Set the sampling period, the controller periodically sends FlowStatisticsRequest messages to the switch, the switch receives the FlowStatisticsRequest message, sends a FlowStatisticsReply message to the controller, and then divides the obtained bytes by the sampling interval to obtain the average information transmission rate within the time interval. These statistics are stored for simulatio...

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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...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/10
CPCG06N3/10G06Q10/04
Inventor 蒋定德齐盛朱相楠乔琛
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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