On a parallel partitioning technique for use with conservative parallel simulation
PADS '93 Proceedings of the seventh workshop on Parallel and distributed simulation
Dynamic load balancing strategies for conservative parallel simulations
Proceedings of the eleventh workshop on Parallel and distributed simulation
An Efficient Partitioning Algorithm for Distributed Virtual Environment Systems
IEEE Transactions on Parallel and Distributed Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Combinatorial Auctions
Quasi-Dynamic Network Model Partition Method for Accelerating Parallel Network Simulation
MASCOTS '06 Proceedings of the 14th IEEE International Symposium on Modeling, Analysis, and Simulation
A Flexible Dynamic Partitioning Algorithm for Optimistic Distributed Simulation
Proceedings of the 21st International Workshop on Principles of Advanced and Distributed Simulation
Algorithmic Game Theory
PADS '10 Proceedings of the 2010 IEEE Workshop on Principles of Advanced and Distributed Simulation
Tight bounds for selfish and greedy load balancing
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part I
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Distributed simulation of large and complex networks requires equitable partitioning of the network model. We propose an iterative partitioning scheme for distributed network simulation that is based on a game theoretic model of simulated network nodes acting as players. We first model the cost function for the nodes and prove that a Nash equilibrium exists for the non-cooperative game in pure strategies. We then propose an iterative algorithm based on our model and prove that it converges to one of the many Nash equilibria. We show with the help of simulations that such a design of cost function moves the system towards optimum partition of the network model as the algorithm converges.