We examine a parallel processing method for simulations of large-scale networks with a hybrid traffic representation combining both a time-stepped fluid model and a discrete-event packet-oriented model. This method benefits from the observation that the time it takes to propagate fluid characteristics along the path taken by the traffic flows has a lower bound equal to the minimum link delay as manifested by the governing ordinary differential equations (ODEs). A better lookahead can thus be used to allow parallel simulation of the hybrid model to run without more synchronization overhead than the corresponding discrete-event packet-oriented model. We derive an analytical model comparing the fluid model and the packet-oriented model both for sequential and parallel simulations. We demonstrate the benefit of the parallel hybrid model through a series of simulation experiments of a large-scale network consisting of over 170,000 hosts and 1.6 million traffic flows on a small parallel cluster.
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