A novel neural computing model for fast predicting network traffic

Liu, Q; Cai, W; Shen, J; Fu, Z; Linge, N

Abstract

Currently existing web traffic prediction models have the shortages of low accuracy, low stability and slow training speed. Aiming at such problems, this paper proposes a new model to predict the network traffic called MRERPM (MapReduce-based ELM Regression Prediction Model). In this prediction model, Extreme Learning Machine is used to accelerate the training speed and improve the accuracy of prediction. Moreover, a distributed cluster is established based on Apache Hadoop to furtherly improve the processing capacity. Experiment results show that MRERM has a large improvement over training speed compared with other models based on K-ELM or SVR, but not at the cost of accuracy.

Citation

Liu, Q., Cai, W., Shen, J., Fu, Z., & Linge, N. (2015). A novel neural computing model for fast predicting network traffic. Journal of Computational and Theoretical Nanoscience, 12(12), 6056-6062. https://doi.org/10.1166/jctn.2015.5076

• Journal Article Type: Article
• Publication Date: Dec 1, 2015
• Deposit Date: Dec 16, 2016
• Journal: Journal of Computational and Theoretical Nanoscience
• Print ISSN: 1546-1955
• Publisher: American Scientific Publishers
• Volume: 12
• Issue: 12
• Pages: 6056-6062
• DOI: https://doi.org/10.1166/jctn.2015.5076
• Publisher URL: http://dx.doi.org/10.1166/jctn.2015.5076