Open AccessA Novel Network Traffic Prediction Method Based on RMPM
Author(s) -
Jianjun Wu,
Wei Gong,
Zhen Shang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1267/1/012067
Subject(s) - computer science , network traffic control , quality of service , artificial neural network , bandwidth (computing) , ethernet , bandwidth allocation , network congestion , key (lock) , traffic generation model , machine learning , traffic flow (computer networking) , traffic classification , data mining , artificial intelligence , computer network , real time computing , network packet , computer security
The network traffic prediction plays the key role in congestion control and bandwidth allocation. A variety of traditional learning models such as artificial neural networks (ANN) have been applied in prediction. To avoid the drawbacks of traditional models for prediction, a novel robust minimax probability machine (RMPM)-based traffic prediction method is proposed in this paper. The prediction performance is tested on two different types of traffic data, Ethernet data flow and MPEG4 video flow, at the timescale 1. The experiments demonstrate that the proposed method attains satisfactory performance in prediction accuracy. Therefore, the proposed method can be used for congestion control or bandwidth allocation, to meet the user QOS requirements.