Mixture Models of Endhost Network Traffic ( IEEE INFOCOM 2013, Torino, IT. April 2013. )

J. M. Agosta, Daniel Ting, Jaideep Chadrashekar, Mark Crovella, Nina Taft

Abstract:

In this work we focus on modeling a little studied
type of traffic, namely the network traffic generated from
endhosts. We introduce a parsimonious parametric model of
the marginal distribution for connection arrivals. We employ
mixture models based on a convex combination of component
distributions with both heavy and light-tails. These models can be
fitted with high accuracy using maximum likelihood techniques.
Our methodology assumes that the underlying user data can be
fitted to one of many modeling options, and we apply Bayesian
model selection criteria as a rigorous way to choose the preferred
combination of components. Our experiments show that a simple
Pareto-exponential mixture model is preferred for a wide range
of users, over both simpler and more complex alternatives.
This model has the desirable property of modeling the entire
distribution, effectively segmenting the traffic into the heavy
tailed as well as the non-heavy-tailed components. We illustrate
that this technique has the flexibility to capture the wide diversity
of user behaviors.