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Context-aware applications pose new challenges, including a need for new computational models, uncertainty management, and efficient optimization under uncertainty. Uncertainty can arise at two levels: multiple and single tasks. When a mobile user changes environments, the context changes resulting in the possibility of the user requesting tasks which are specific for the new environment. However as the user moves these requested tasks may no longer be context relevant. Additionally, the runtime of each task is often highly dependent on the input data. We introduce an hierarchical multi-resolution statistical task model that captures relevant aspects at the task and intertask levels, and captures not only uncertainty, but also introduces the notion of utility for the user. We have developed a system of nonparametric statistical techniques for modeling the runtime of a specific task. This model is a framework where we define problems of design and optimization of statistical soft real-time systems (SSRTS). The main algorithmic novelty is a cumulative potential-based task scheduling heuristic for maximizing utility. The heuristic conducts global optimization and induces low runtime overhead. We demonstrate the effectiveness of the scheduling heuristic using a Trimaran-based evaluation platform.