NDLI: Short-term forecasting model of web traffic based on genetic algorithm and neural network

Content Provider	IEEE Xplore Digital Library
Author	Meimei Chen
Copyright Year	2011
Description	Author affiliation: Dept. of E-Commerce and Logistic, Glorious Sun School of Business and Management, Donghua University, Shanghai, China (Meimei Chen)
Abstract	Network traffic is an important load indicator that reflects the performance of the web system. Short-term forecast of web traffic is the base of effective overload control. Because of the complex and ever-changing network environment, web traffic is shown the characteristics of random and unexpected at most of the time scales. Hence, it is more difficult to improve the accuracy of traffic forecasts to get satisfactory results. In this paper, genetic algorithm is used in artificial neural network to optimize the structure design and weights firstly. Then, a web traffic forecasting model based on genetic neural network is proposed. The simulation result shown that the forecast result of this model is better than that based on BP and Elman neural network prediction model.
Starting Page	623
Ending Page	626
File Size	355910
Page Count	4
File Format	PDF
ISBN	9781457705359
e-ISBN	9781457705366
DOI	10.1109/AIMSEC.2011.6010375
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Genetic algorithm Network traffic Artificial neural networks Web system Predictive models Wavelet analysis Forecasting model Artificial neural network Forecasting Genetic algorithms
Content Type	Text
Resource Type	Article

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