Their uncritical use results in poor fit to real-life networks and a subsequent low predictive power. Generally, the simulation time complexity is linear in the transmission speed, the size of the network, the traffic intensity and the simulated time horizon. It is important to provide for simulation speedup as well as storage savings to admit large and complex networks. To reduce the number of simulation events, and consequently the run time of network simulation, we propose to add a fluid-flow strand to traditional queueing. A fluid-flow model is a macroscopic representation of fluid flows among storage tanks.
Fluid volumes represent workloads, corresponding to virtual waiting times in queueing parlance.
In this case, simulating a single fluid-flow node requires relatively little computational resources. Assuming a fixed service rate, the arrival rate can be often expected to vary on the order of seconds. We hasten to add at this juncture that the fluid-flow paradigm is not a modeling panacea. A more serious problem than loss of modeling power is the fact that the simulation time complexity of fluid-flow networks is not linear in the size of the network, and the event processing time is no longer bounded as function of network size. In a nutshell, a fluid-flow model with multiple flows and complex topology can experience a multitude of rate changes, each of which must be processed as an event.