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In this work we define and test a new framework obtained as the integration of two recently developed middlewares defined to support the parallel and distributed simulation of large scale, complex and dynamically interacting system models (like wireless and mobile network systems). In a distributed simulation of highly interacting system models, the main bottleneck may become the communication and synchronization required to maintain the causality constrains between distributed model components. We designed and implemented the ARTÌS middleware as a new framework incorporating a set of features that allow an adaptive optimization of the communication layer management in a distributed simulation scenario. ARTÌS has been integrated with GAIA, a dynamic mechanism for the runtime management and adaptive allocation of model entities in a distributed simulation. By adopting a runtime evaluation of causal bindings between model entities GAIA adapts the dynamic and time-persistent causal effects of model interactions to dynamic migration of model entities. Preliminary results demonstrate that the combined effect of ARTÌS management and GAIA heuristics leads to a significant reduction in the communication and synchronization overheads between the physical execution units. Simulation performance enhancements have been obtained also in worst-case modelling assumptions and simulation scenarios.