Paper Info
Title
Gaussian Process Regression Based Traffic Modeling and Prediction in High-Speed Networks.
Abstract
Evolving nature of network traffic challenges existing models to fit and predict its behavior. In particular, real traffic modeling requires more flexible design that can adapt to long-range and short-range dependent traffic with dynamic patterns. Unfortunately, existing models cannot handle such requirements because various traffic behaviors such as periodic and self-similar are not taken into account. In this paper, Gaussian process regression (GPR) is adapted for traffic modeling and prediction. The connection between self-similarity as a traffic characteristic and GPR parameters has been driven and exerted to build of a new Hurst estimation method based on machine learning techniques. This led to propose self-similar covariance functions for enhancing prediction accuracy of GPR. The proposed GPR model has been applied for Hurst estimation as well as for traffic prediction on real traffic traces at different time-scales. The experimental results show the employment of self-similar covariance functions increases generalization ability of GPR for traffic modeling and prediction.
Year
Venue
Keywords
2016
IEEE Global Communications Conference
Traffic Modeling and Prediction,Long-Range Dependency (LRD),Gaussian Process Regression (GPR),Hurst Exponent Estimation
Field
DocType
ISSN
Data mining,Ground-penetrating radar,Computer science,Real-time computing,Prediction algorithms,Gaussian process,Artificial intelligence,Traffic prediction,Covariance,Kriging,Traffic generation model,Periodic graph (geometry),Machine learning
Conference
2334-0983
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Abdolkhalegh Bayati101.35
Vahid Asghari220216.39
Kim Khoa Nguyen311524.33
Mohamed Cheriet42047238.58