Time-parallel generation of self-similar ATM traffic

We present a time-parallel technique for the fast generation of self-similar traffic which is suitable for performance studies of Asynchronous Transfer Mode (ATM) networks. The technique is based on the well known result according to which the aggregation of a large number of heavy-tailed ON/OFF-type renewal/reward processes asymptotically approximates a Fractional Gaussian Noise (FGN) process and, therefore, it possesses the characteristics of self-similarity and long-range dependence. The technique parallelizes both the generation of the individual renewal/reward processes as well as tile merging of these processes in a per-time-slice. manner. Results obtained from a message-passing implementation on a cluster of workstations confirm that it is possible to generate self-similar ATM traffic in realtime for 155 Mbps (or even faster) links and that, furthermore, the technique achieves an almost linear speedup with respect to the number of available workstations.