Integrating rising variability of software systems in performance prediction models is crucial to allow widespread industrial use of performance prediction. One of such variabilities is the dependency of system performance on the context and history-dependent internal state of the system (or its components). The questions that rise for current prediction models are (i) how to include the state properties in a prediction model, and (ii) how to balance the expressiveness and complexity of created models.Only a few performance prediction approaches deal with modelling states in component-based systems. Currently, there is neither a consensus in the definition, nor in the method to include the state in prediction models. For these reasons, we have conducted a state-of-the-art survey of existing approaches addressing their expressiveness to model stateful components. Based on the results, we introduce a classification scheme and present the state-defining and state-dependent model parameters. We extend the Palladio Component Model (PCM), a model-based performance prediction approach, with state-modelling capabilities, and study the performance impact of modelled state. A practical influences of the internal state on software performance is evaluated on a realistic case study.