In this paper, we address the problem of symbolically computing the region in the parameter's space that guarantees a feasible schedule, given a set of real-time tasks characterised by a set of parameters and by an activation pattern.We make three main contributions. First, we propose a novel and general method, based on parametric timed automata. Second, we prove that the algorithm terminates for the case of periodic processes with bounded offsets. Third, we provide an implementation based on the use of symbolic model checking techniques for parametric timed automata, and present some case studies.
This paper advocates a rigorously formal and compositional style for obtaining key performance and/or interface metrics of systems with real-time constraints. We propose a hierarchical approach that couples the independent and different by nature frameworks of Modular Performance Analysis with Real-time Calculus (MPA RTC) and Parametric Feasibility Analysis (PFA). Recent work on Real-time Calculus (RTC) has established an embedding of state based component models into RTC-driven performance analysis for dealing with more expressive component models. However, with the obtained analysis infrastructure it is possible to analyze components only for a fixed set of parameters, e. g., fixed CPU speeds, fixed buffer sizes etc., such that a big space of parameters remains unstudied. In this paper, we overcome this limitation by integrating the method of parametric feasibility analysis in an RTC based modeling environment. Using the PFA tool-flow, we are able to find regions for component parameters that maintain feasibility and worst-case properties. As a result, the proposed analysis infras tructure produces a broader range of valid design candidates, and allows the designer to reason about the system robustness.
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