Simulating and understanding traffic dynamics in large networks are difficult and challenging due to the complexity of such networks and the limitations inherent in simulation modelling. Typically, simulation models used to study traffic dynamics include substantial detail representing protocol mechanisms across several layers of functionality. Such models must be restricted in space and time in order to be computationally tractable. We propose an alternative simulation approach that uses homogeneous modelling with an increased level of abstraction, in order to explore networks at larger space-time scales than otherwise feasible and to develop intuition and insight about the space-time dynamics of large networks. Traffic modelling is the problem of representing our understanding of dynamic demands by stochastic processes. Accurate traffic models are necessary for service providers to properly maintain quality of service. Many traffic models have been developed based on traffic measurement data. Sources are sometimes policed or regulated at the network access, usually by a leaky-bucket algorithm. Access policing can change the shape of source traffic by limiting the peak rate or burstiness. Source traffic may also be regulated by protocol mechanisms such as sliding windows or congestion windows (as in TCP), leading to other traffic models. Key words: MODELING, NETWORK TRAFFIC; SIMULATION; WAVELET- BASED ANALYSIS, LONG RANGE DEPENDENCE, SELF SIMILARITY, STOCHASTIC FLUID, TIME SERIES, POISSON PROCESS, MARKOV MODULATED PROCESS
