Evaluating and improving the performance of anonymity systems in a real-world setting is critical to foster their adoption. However, current research in this field mostly employs unrealistic models for evaluation purposes. Moreover, previously documented results are often difficult to reproduce. We propose two complementary workload models that operate on network traces in order to improve the evaluation of anonymity systems. In comparison to other approaches our workload models are more realistic, as they derive characteristics from trace files recorded in real networks and preserve dependencies of the flows of individual hosts. We also describe our ready-to-use open source evaluation suite that implements our models. Given our tools, researchers can easily create and re-use well-defined workload sets for evaluation purposes. Finally, we demonstrate the importance of realistic workload models by evaluating a well-known dummy traffic scheme with our tools.