A Source traffic model and its transient analysis for network control

Traffic measurements from communication networks have shown that network traffic is quite complex, exhibiting phenomena such as long-tail probability distributions, long-range dependence and self similarity. Thus, in order to design and control new communication networks, we are motivated to consider new source traffic models and new ways to analyze network performance when there are many independent sources each with traffic that can be described by such models. We present a candidate source traffic model: The required bandwidth (arrival rate) as a function of time for each source is represented as the sum of two stochastic processes: (1) a macroscopic (longer-time-scale) level process and (2) a microscopic (shorter-time-scale) within-level variation process. We let the level process be a finite-state semi-Markov process (SMP), allowing general (possibly long-tail) level holding-time distributions, and we let the within-level variation process be a zero-mean piecewise-stationary process. To cope with the added model complexity, we suggest making design and control decisions based on the likelihood that aggregate demand (the input rate from a set of sources) will exceed capacity (the maximum possible output rate). This approach to model analysis avoids the customary queueing detail (focus on buffer content and overflow). We propose using transient analysis for control, conditioning on the level and elapsed level holding time of each source. In doing so, we exploit asymptotics associated with multiplexing a large number of sources.