To meet the demands of intelligent network management in increasingly congested network environments, high-precision network traffic prediction is crucial. In order to enhance prediction accuracy, a network traffic prediction model incorporating Firefly Algorithm optimized Support Vector Machine (SVM) has been proposed. Firstly, simulation is performed based on the statistical properties of self-similar network traffic, and the Firefly Algorithm is employed to rapidly search for the optimal parameters of SVM. With these optimal parameters, the simulated network traffic is learned to derive an optimal network traffic prediction model. Finally, a comparative analysis is conducted between the optimized SVM-based network traffic prediction model and the non-optimized model. Experimental results demonstrate that this approach achieves higher prediction accuracy in network traffic forecasting while meeting the requirements of network management.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Network Traffic Prediction Based on Firefly Algorithm Optimized Support Vector Machine


    Contributors:
    Cong, Yuyuan (author) / Li, Linlin (author) / Guo, Yibing (author)


    Publication date :

    2024-09-20


    Size :

    1416316 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A support vector machine–firefly algorithm-based model for global solar radiation prediction

    Olatomiwa, Lanre / Mekhilef, Saad / Shamshirband, Shahaboddin et al. | Tema Archive | 2015


    Traffic fatalities prediction based on support vector machine

    Ting Li / Yunong Yang / Yonghui Wang et al. | DOAJ | 2016

    Free access

    Application on Network Traffic Prediction Based on Least Squares Support Vector Machine

    Ren, Yu Zhuo ;Xia, Ke Wen ;Wang, Yan | Trans Tech Publications | 2010