The performance of the MPI's collective communications is critical in most MPI-based applications. A general algorithm for a given colletive communication operation may not give good performance on all systems due to the differences in architectures, network parameters and the storage capacity of the underlying MPI implmentation. Hence, collective communications have to be tuned for the system on which they will be executed. In order to determine the optimum parameters of collective communications on a given system in a time-efficient manner, the collective communications need to be modeled efficiently. In this paper, we discuss various techniques for modeling collective communications.