Name
Abstract | ||
|---|---|---|
We model a little studied type of traffic, namely the network traffic generated from endhosts. We introduce a parsimonious model of the marginal distribution for connection arrivals consisting of mixture models with both heavy and light-tailed component distributions. Our methodology assumes that the underlying user data can be fitted to one of several models, and we apply Bayesian model selection criterion to choose the preferred combination of components. Our experiments show that a simple Pareto-exponential mixture model is preferred over more complex alternatives, for a wide range of users. This model has the desirable property of modeling the entire distribution, effectively clustering the traffic into the heavy-tailed as well as the non-heavy-tailed components. Also this method quantifies the wide diversity in the observed endhost traffic. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/INFCOM.2013.6566768 | international conference on computer communications |
Keywords | DocType | ISSN |
Bayes methods,Pareto distribution,telecommunication networks,telecommunication traffic,Bayesian model selection criterion,connection arrivals,endhost network traffic,heavy-tailed component distributions,light-tailed component distributions,marginal distribution,parsimonious model,simple Pareto-exponential mixture,traffic clustering,wide diversity | Journal | 0743-166X |
ISBN | Citations | PageRank |
978-1-4673-5944-3 | 2 | 0.45 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J.M. Agosta | 1 | 2 | 0.45 |
| J. Chandrashekar | 2 | 2 | 0.45 |
| Mark Crovella | 3 | 5562 | 675.80 |
| Nina Taft | 4 | 2109 | 154.92 |