The problem of real-time processing is one of the most challenging current issues in computer sciences. Because of the large amount of data to be treated in a limited period of time, parallel and distributed systems are required, whose performance depends on a series of factors including the interconnectivity of the processing elements, the application model and the communication protocol. Given their flexibility for representing and modeling natural and human-made systems (such as the Internet and WWW), complex networks have become a primary choice in many research areas. The current work presents how the concepts and methods of complex networks can be used to develop realistic models and simulations of distributed real-time system while taking into account two representative interconnection models: uniformly random and scale free (Barabasi-Albert), including the presence of background traffic of messages. The interesting obtained results include the identification of the uniformly random interconnectivity scheme as being largely more efficient than the scale-free counterpart.