VoIP Traffic Modelling Using Gaussian Mixture Models, Gaussian Processes and Interactive Particle Algorithms

The paper deals with an important problem in the Voice over IP (VoIP) domain, namely being able to understand and predict the structure of traffic over some given period of time. VoIP traffic has a time variant structure, e.g. due to sudden peaks, daily or weekly moving patterns of activities, which in turn makes prediction difficult. Obtaining insights about the structure and trends of traffic has important implications when dealing with the nowadays cloud-deployed VoIP services. Prediction techniques are applied to anticipate the incoming traffic, for an efficient distribution of the traffic in the system and allocation of resources. The article looks in a critical manner at a series of machine learning techniques. We namely compare and review (using real VoIP data) the results obtained when using a Gaussian Mixture Model (GMM), Gaussian Processes (GP), and an evolutionary-like Interacting Particle Systems based (sampling) algorithm. The experiments consider different setups as to verify the time variant traffic assumption.

[1]  Steven W. Nydick,et al.  The Wishart and Inverse Wishart Distributions , 2012 .

[2]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[3]  Ohnmar Nhway.  An investigation into the effect of security on reliability and voice recognition system in a VoIP network , 2011, 13th International Conference on Advanced Communication Technology (ICACT2011).

[4]  Abderrahmane Amrouche,et al.  Fusion strategies for distributed speaker recognition using residual signal based G729 resynthesized speech , 2013, Proceedings of the 16th International Conference on Information Fusion.

[5]  R. Lathe.  Phd by thesis , 1988, Nature.

[6]  Pierre Del Moral,et al.  On the Foundations and the Applications of Evolutionary Computing , 2013, EVOLVE.

[7]  Trang Dinh Dang,et al.  Fractal analysis and modeling of VoIP traffic , 2004, 11th International Telecommunications Network Strategy and Planning Symposium. NETWORKS 2004,.

[8]  Geoffrey J. McLachlan,et al.  Mixture models : inference and applications to clustering , 1989 .

[9]  William L. Root,et al.  The Gaussian Process , 1987 .

[10]  Pascal Bouvry,et al.  Predictive Modeling in a VoIP System , 2013 .

[11]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[12]  Douglas A. Reynolds,et al.  Gaussian Mixture Models , 2018, Encyclopedia of Biometrics.

[13]  Carol Ash.  Continuous Random Variables , 1993 .

[14]  P. Moral,et al.  Particle methods: An introduction with applications , 2014 .

[15]  Eric R. Ziegel,et al.  Engineering Statistics , 2004, Technometrics.

[16]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[17]  Bingxi Wang,et al.  Two Schemes for Automatic Speaker Recognition over VOIP , 2008, PACIIA.