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Agent-Based Models (ABMs) are a class of models which, by simulating the behavior of multiple agents (i.e., ndependent actions, interactions and adaptation), aim to emulate and/or predict complex phenomena. One of the general features of ABM simulations is their experimental capacity, that requires a viable and reliable infrastructure to interact with a running simulation, monitoring its behaviour, as it proceeds, and applying changes to the configurations at run time, (the computational steering) in order to study "what if" scenarios. A common approach for improving the efficiency and the effectiveness of ABMs as a research tool is to distribute the overall computation on a number of machines, which makes the computational steering of the simulation particularly challenging. In this paper, we present the principles and the architecture design of the management and control infrastructure that is available in D-Mason, a framework for implementing distributed ABM simulations. Together with an efficient parallel distribution of the simulation tasks, D-Mason offers a number of facilities to support the computational steering of a simulation, i.e. monitoring and interacting with a running distributed simulation.