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.