Modeling heterogeneous sources on multiple time scales

We develop parameters and results for a set of traffic models which emulate the traffic patterns at the packet and buffer levels. We show that traffic models at these levels are dependent on the network capacity as well as source-induced bottlenecks such as buffer reads and writes. We propose a pragmatic approach which uses three variations of a simple two-state Markov superposition model to describe the buffer and packet level view of the traffic under different bottleneck conditions. We apply these models to actual network traffic sources as measured on a medical network and evaluate the impact of changing bottlenecks in network transfers. Our data and results emphasize the sensitivity of any traffic model to the underlying machine capabilities and the timescale of interest. These models can be used to characterize a wide range of traffic types, and allow relatively simple analytic and simulation implementation