the optimal system. The actual process of developing the simulation can itself provide valuable insights into the inner workings of the network which can in turn be used at a later stage. Disadvantages of simulation Accurate simulation model development requires extensive resources. The simulation results are only as good as the model and as such are still only estimates / projected outcomes. Optimisation can only be performed involving a few alternatives as the model is usually developed using a limited number of variables. Simulations cost a lot of money to build and are very expensive to make Statistical issues in simulation modelling Input data Simulation models are generated from a set of data taken from a stochastic system. It is necessary to check that the data is statistically valid by fitting a statistical distribution and then testing the significance of such a fit. Further, as with any modelling process, the input data’s accuracy must be checked and any outliers must be removed. Output data When a simulation has been completed, the data needs to be analysed. The simulation's output data will only produce a likely estimate of real-world events. Methods to increase the accuracy of output data include: repeatedly performing simulations and comparing results, dividing events into batches and processing them individually, and checking that the results of simulations conducted in adjacent time periods “connect” to produce a coherent holistic view of the system. Random numbers As most systems involve stochastic processes, simulations frequently make use of random number generators to create input data which approximates the random nature of real-world events. Computer generated [random numbers] are usually not random in the strictest sense, as they are calculated using a set of equations. Such numbers are known as pseudo-random numbers. When making use of pseudo-random numbers the analyst must make certain that the true randomness of the numbers is checked. If the numbers are found not to behave in a sufficiently random fashion, another generation technique must be found. Random numbers for the simulation are created by a random number generator. See also Channel model Network simulation Network simulator Mobility models Traffic generation model Simulation language Queueing theory References Computer network analysis Stochastic simulation