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Cyber-physical systems (CPS) represent the information technology quest of the 21-st century for a better, cleaner, safer life by integrating computation, communication, and control with physical processes. Physical processes are ubiquitously non-stationary and require time-dependent models for modeling and understanding their behavior. In contrast, most current computing platforms and their design methodologies lack proper models for the time component and mostly assume stationary (i.e., time independent) behavior. In this paper, we use empirical data to identify the main characteristics (e.g., self-similarity, nonstationarity) of various physical processes which can also be observed in the communication workload of real CPS. Starting from the complex characteristics of CPS workloads, we present a statistical physics inspired model which is used to define a new optimal control problem that not only accounts for the observed self-similarity and nonstationarity properties of the CPS workload, but also allows for accurate predictions on CPS dynamical trajectories during the optimization process.