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These deadlines and workloads are dynamic and uncontrollable and the systems typically have only a limited discrete number of system configurations to respond to dynamic changes. The number and types of processors allocated to a data-processing task, their operating voltage and frequency, and the resolution and frequency of sensing (e.g., images) are examples of controllable configuration parameters. Guaranteeing dynamically changing deadlines under uncontrollable workloads with a limited discrete number of response options can be phrased as a multi-objective tracking problem for a switched max-plus linear system. This results in a combined scheduling and control problem. We propose an integrated state-feedback and model-predictive control solution that minimizes the number of deadline misses and the cost of implementation (e.g., energy consumption). 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