ACM Transactions on Programming Languages and Systems (TOPLAS)
Calendar queues: a fast 0(1) priority queue implementation for the simulation event set problem
Communications of the ACM
Parallel and Distribution Simulation Systems
Parallel and Distribution Simulation Systems
Computing in Science and Engineering
Scalable RTI-Based Parallel Simulation of Networks
Proceedings of the seventeenth workshop on Parallel and distributed simulation
Integrated Fluid and Packet Network Simulations
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
A federated approach to distributed network simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Parallel simulation techniques for large-scale networks
IEEE Communications Magazine
Performance evaluation of a CMB protocol
Proceedings of the 38th conference on Winter simulation
A model for estimating the performance of synchronous parallel network simulation
International Journal of Modelling and Simulation
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Simulation of large-scale networks requires enormous amounts of memory and processing time. One way of speeding up these simulations is to distribute the model over a number of connected workstations. However, this introduces inefficiencies caused by the need for synchronization and message passing between machines. In distributed network simulation, one of the factors affecting message passing overhead is the amount of cross-traffic between machines. We perform an independent benchmark of theParallel/Distributed Network Simulator (PDNS) based on experimental results processed at the Australian Centre for Advanced Computing and Communications (AC3) supercomputing cluster. We measure the effect of cross-traffic on wall-clock time needed to complete a simulation for a set of basic network topologies by comparing the result with the wall-clock time needed on a single processor. Our results show that although efficiency is reduced with large amounts of cross-traffic, speedup can still be achieved with PDNS. With these results, we developed a performance model that can be used as a guideline for designing future simulations.