Research Of A Load Balancing Algorithm Based On Traffic Prediction In Cognitive Network

In the traditional network,the problems of low utilization of resources,the delay of service and the poor effect of load balancing are obvious,and the emergence of cognitive network provides the possibility to solve the above problems.Compared with the traditional network,cognitive network has the characteristics of learning and reasoning and intelligent decision,through the real-time and dynamic sensing network status,network intelligent planning,parameter configuration and traffic scheduling,effectively improve the utilization of network resources,and provide a higher quality of service.This paper presents a load balancing algorithm combined improved neural network traffic prediction model based on cognitive network,focusing on solving problem of unbalanced network load.For example,light load area does not make full use of free resources,heavy load area congestion occurs,which leads to the increase of traffic delay and the increase of packet loss rate and so on.Firstly,this paper introduces the concept and model of cognitive network,and makes a deep research on the self learning and adaptive characteristics of cognitive network.At the same time,the research status of traffic prediction and load balancing technology are deeply discussed.Secondly,this paper introduces the three commonly used combination neural network model and its basic algorithm ideas,and the wavelet neural network traffic flow prediction model is chosen to improve.According to the problem of over fitting of wavelet neural network and easy to fall into local optimum and flat spot area,a new traffic prediction model,namely WFLNN model,is proposed.Firstly,the data samples are processed by BP network,and the processed data samples are implemented by wavelet transform.Then the high frequency and low frequency components obtained by wavelet transform are used as input of RBF neural network and Elman network respectively,and fast learning neural network algorithm is used to predict.Among them,the FLBP algorithm is a new algorithm to improve the BP neural network by using three kinds of fast learning algorithms.It uses a systematic approach to check whether the learning process is trapped in a local optimum or a flat spot,and then jump over them,in order to keep looking for a suitable method to reach the global optimum,and further improve the prediction accuracy.At the same time,the WFLNN model and WNN model are compared with the MATLAB model,and the simulation results show that the prediction accuracy of WFLNN model is higher.Finally,because the traditional network can not be real-time and dynamically aware of the current state of the network and can not configure the server parameters in advance,so the network delay is obvious and so on.Therefore,this paper designs a load balancing algorithm based onWFLNN prediction model in cognitive network.This algorithm uses the cognitive,reasoning and intelligent and intelligent decision of cognitive network to perceive the current situation of the network,configure network parameters in advance according to the result of flow prediction,and combined with the weighted minimum connection scheduling algorithm to schedule the network traffic,to achieve network load balancing.At the same time,the software OPNET is used to simulate,the results show that the load balancing algorithm based on WFLNN prediction model in cognitive network has better load balancing effect compared with the unimproved algorithm in the general network.