SDN flow prediction method based on an ARMA mode

A technology of traffic forecasting and modeling, applied in biological neural network models, computer simulations, complex mathematical operations, etc., can solve the problems of inability to describe the long-term correlation of network traffic, low network flexibility and intelligence, and degradation of self-similar model prediction performance And other issues

Inactive Publication Date: 2019-03-22
UNIV OF ELECTRONIC 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 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

Method used

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  • SDN flow prediction method based on an ARMA mode
  • SDN flow prediction method based on an ARMA mode
  • SDN flow prediction method based on an ARMA mode

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Embodiment

[0118] To perform traffic prediction, first collect the flow statistics required for simulation. According to the introduction to OpenFlow, it can be known that the controller sends OpenFlow messages to query the information stored in the counter of the flow entry in the OpenFlow switch, so as to obtain flow statistics. The counter records how many packets belonging to this flow have been received, and other statistical data (such as the number of packets sent and received, the number of bytes sent and received, and the number of searches).

[0119] The research of the present invention is to model and analyze a network flow in the network, so we need to select this flow first, create a new flow table item for this flow, and then perform real-time measurement. The real-time measurement mentioned here is to collect the statistical information of this flow on the counter. The flow chart of traffic modeling algorithm based on ARMA model is as follows: figure 1 As shown, the sp...

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Abstract

The invention discloses an SDN flow prediction method based on an ARMA model, belonging to the technical field of wireless communication network, in particular to a performance analysis and network planning method suitable for a network. Aiming at the shortcomings of the prior art, the invention can accurately predict the flow change trend of the SDN. The algorithm obtains simulation data from counters in OpenFlow switches by sampling, smoothes the data, trains ARMA model by using the smoothed data, and obtains ARMA model parameters, including autoregressive coefficients, moving average coefficients and order of the model. After obtaining the parameters, an ARMA forecasting model is established, and the trend of SDN traffic is forecasted by the established model.

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 Networks (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 algorit...

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Application Information

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IPC IPC(8): G06N3/10G06F17/18
CPCG06F17/18G06N3/105
Inventor 蒋定德王键王雨晴齐盛
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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