Simulation has become an important way to observe and understand various networking phenomena under various conditions. As the demand to simulate larger and more complex networks increases, the limited computing capacity of a single workstation and the limited simulation capability of a single network simulator have become apparent obstacles to the simulationists. In this research we develop techniques that can scale a simulation to address the limited capacity of a single workstation, as well as techniques that can compose a simulation from different simulator components to address the limited capability of a single network simulator. We scale a simulation with two different approaches: 1) We reduce the resource requirement of a simulation substantially, so that larger simulations can fit into one single workstation. In this thesis, we develop three technqiues (Negative Forwarding Table, Multicast Routing Object Aggregation and NIx-Vector Unicast Routing) to aggregate and compress the large amount of superfluous or redundant routing state in large multicast simulations. 2) The other approach to scale network simulations is to partition a simulation model in a way that makes the best use of the resources of the available computer cluster, and distribute the simulation onto the different processors of the computer cluster to obtain the best parallel simulation performance. We develop a novel empirical methodology called BencHMAP (Benchmark-Based Hardware and Model Aware Partitioning) that runs small sets of benchmark simulations to derive the right formulas of calculating the weights that are used to partition the simulation on a given computer cluster. On the other hand, to address the problem of the limited capability of a network simulator, we develop techniques for building complex network simulations by composing from independent components. With different existing simulators good at different protocol layers/scenarios, we can make each simulator execute the layers where it excels, using a simulation backplane to be the interface between different simulators. In this thesis we demonstrate that these techniques enable us to not only scale up simulations by orders of magnitude with a good performance, but also compose complex simulations with high fidelity.