Estimating probabilistic timing performance for real-time embedded systems

In system-level design of real-time embedded systems, being able to capture the interactions among the tasks with respect to timing constraints and determine the overall system timing performance is a major challenge. Most previous works in the area are either based on a fixed execution time model or are only concerned with the probabilistic timing behavior of each individual task. The few papers that deal with overall system probabilistic behavior have used improper assumptions. In this paper, given that the execution time of each task is a discrete random variable, a novel concept of state is introduced based on a new metric that is derived that measures the probability of a task set being able to be scheduled. Several approaches to evaluating the metric are also presented. Applying this metric in the system-level design exploration process, one can readily compare the probabilistic timing performance of alternative designs.