Title :
Neural networks for web server workload forecasting
Author :
Tran, Van Giang ; Debusschere, Vincent ; Bacha, Seddik
Author_Institution :
Univ. Grenoble Alpes, G2Elab, Grenoble, France
Abstract :
This paper presents a comparative study of five intelligent forecast models for workload of server defined as HTTP requests. These five forecast models are based on the methodology: Nonlinear AutoRegressive model with eXogenous Inputs (NARX), Multilayer Perceptron (MLP), Elman, Cascade-Neural Network (CCNN) and Pattern Recognition Neural Network (PRNN). The best accuracy prediction is given by the NARX model. This work takes parts in development of our forecast models in the project EnergeTic-FUI, France.
Keywords :
autoregressive processes; file servers; multilayer perceptrons; pattern recognition; CCNN; Elman neural network; EnergeTic-FUI; France; HTTP requests; MLP; NARX; PRNN; Web server workload forecasting; cascade-neural network; intelligent forecast models; multilayer perceptron; nonlinear autoregressive model with exogenous inputs; pattern recognition neural network; Computational modeling; Forecasting; Hidden Markov models; Neural networks; Neurons; Predictive models; Servers; EnergeTIC-FUI; Neural network; data center workload forecasting; intelligent computational; server workload;
Conference_Titel :
Industrial Technology (ICIT), 2013 IEEE International Conference on
Conference_Location :
Cape Town
Print_ISBN :
978-1-4673-4567-5
Electronic_ISBN :
978-1-4673-4568-2
DOI :
10.1109/ICIT.2013.6505835
