The amount of detail to include in a performance model is usually regarded as a judgment to be made by an expert modeler and the question "how much detail is necessary?" is seldom asked and is difficult to answer. However, if a simpler model gives essentially the same performance predictions, it may be more useful than a detailed model. It may solve more quickly, for instance, and may be easier to understand. Or a model for a complex sub-system such as a database server may be usefully simplified so it can be included in larger system models. This research proposes an aggregation process for layered queuing models that reduces the number of queues (called tasks and hosts/processors, in layered models) while preserving the total execution demand and the bottleneck characteristics (the highest saturated resource(s)) of the detailed model. This thesis demonstrates how the simplification process can greatly reduce the number of tasks and processors with a very small relative error. The application of proposed simplification process is applied on a number of case studies. A Java application called "LQN model simplifier" was built that takes an LQN model as input and generates a series of simplified models after applying simplification operations to the original model.