Characterization of Host-Level Application Traffic with Multi-Scale Gamma Model

In this paper we discuss the validity of the multi scale gamma model and characterize the differences in host level application traffic with this model by using a real traffic trace collected on a 150 Mbps transpacific link First we investigate the dependency of the model (parameters alpha and beta and fitting accuracy epsilon) on time scale Delta then find suitable time scales for the model Second we inspect the relations among alpha beta and epsilon in order to characterize the differences in the types of applications The main findings of the paper are as follows (1) Different types of applications show different dependencies of alpha beta and epsilon on Delta and display different suitable Delta s for the model The model is more accurate if the traffic consists of intermittently sent packets than other (2) More appropriate models are obtained with specific alpha and beta values (e g 0 1 < alpha < 1 and beta < 2 for Delta = 500 ms) Also application specific traffic presents specific ranges of alpha beta and epsilon for each Delta so that these characteristics can be used in application identification methods such as anomaly detection and other machine learning methods