  High fidelity simulation of large-sized complex networks can be realized on a
distributed computing platform that leverages the combined resources of
multiple processors or machines. In a discrete event driven simulation, the
assignment of logical processes (LPs) to machines is a critical step that
affects the computational and communication burden on the machines, which in
turn affects the simulation execution time of the experiment. We study a
network partitioning game wherein each node (LP) acts as a selfish player. We
derive two local node-level cost frameworks which are feasible in the sense
that the aggregate state information required to be exchanged between the
machines is independent of the size of the simulated network model. For both
cost frameworks, we prove the existence of stable Nash equilibria in pure
strategies. Using iterative partition improvements, we propose game theoretic
partitioning algorithms based on the two cost criteria and show that each
descends in a global cost. To exploit the distributed nature of the system, the
algorithm is distributed, with each node's decision based on its local
information and on a few global quantities which can be communicated
machine-to-machine. We demonstrate the performance of our partitioning
algorithm on an optimistic discrete event driven simulation platform that
models an actual parallel simulator.
