Path lookahead: a data flow view of PDES models
PADS '99 Proceedings of the thirteenth workshop on Parallel and distributed simulation
Exploiting model independence for parallel PCS network simulation
PADS '99 Proceedings of the thirteenth workshop on Parallel and distributed simulation
Impact of channel models on simulation of large scale wireless networks
MSWiM '99 Proceedings of the 2nd ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
An empirical study of conservative scheduling
PADS '00 Proceedings of the fourteenth workshop on Parallel and distributed simulation
Parallel shared-memory simulator performance for large ATM networks
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Efficient wireless network simulations with detailed propagation models
Wireless Networks - Special issue: Design and modeling in mobile and wireless systsems
Partitioning parallel simulation of wireless networks
Proceedings of the 32nd conference on Winter simulation
Partitioning PCS Networks for Distributed Simulation
HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
MAYA: Integrating hybrid network modeling to the physical world
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques
Proceedings of the 2013 Summer Computer Simulation Conference
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Simulation of large sets of mobile computers or other wireless communication devices is difficult because of the computationally intensive models of wireless channels. Parallel simulation would seem to be applicable here because of the large computation granularity, but the location based communication topology makes conservative methods difficult to implement. This paper considers a novel approach to improving lookahead in conservative parallel wireless network simulations by differentiating between data flow paths in the simulation. An experimental study to evaluate the effectiveness of the technique is also presented. The study shows that for this application, the technique produces a 70% reduction in null message traffic, with only a 25% increase in null message computation overhead. Parallel execution times of the model also show consistent improvement.