  Distributing agent-based simulators reveals many challenges while deploying
them on a hybrid cloud infrastructure. In fact, a researcher's main motivations
by running simulations on hybrid clouds, are reaching more scalable systems as
well as reducing monetary costs. Indeed, hybrid cloud environment, despite
providing scalability and effective control over proper data, requires an
efficient deployment strategy combining both an efficient partitioning
mechanism and cost savings. In this paper, we propose a cost deployment model
dedicated to distributed agent-based simulation systems. This cost model,
combining general performance partitioning criteria as well as monetary costs,
is used to evaluate cluster and grid based partitioning algorithms on hybrid
cloud environments. The first experimental results show that, for a given
agent-based model, a good partitioning method used with the suitable hybrid
cloud environment lead to an efficient and economic deployment.
