Response time central-limit and failure rate estimation for stationary periodic rate monotonic real-time systems
For designers of safety-critical real-time systems, this work provides a statistical approach to estimate failure rates, though it is an incremental improvement over existing methods.
This paper proposes a method to estimate failure rates in stationary periodic rate monotonic real-time systems by approximating the distribution of response times using an inverse Gaussian mixture model. Simulations show the method is well-suited for failure rate approximation.
Real-time systems consist of a set of tasks, a scheduling policy, and a system architecture, all constrained by timing requirements. Many everyday embedded systems, within devices such as airplanes, cars, trains, and spatial probes, operate as real-time systems. To ensure safe failure rates, response times-the time required for the exection of a task-must be bounded. Rate Monotonic real-time systems prioritize tasks according to their arrival rate. This paper focuses on the use of the central limit of response times built in \cite{zagalo2022} and an approximation of their distribution with an inverse Gaussian mixture distribution. The distribution parameters and their associated failure rates are estimated through a suitable re-parameterization of the inverse Gaussian distribution and an adapted Expectation-Maximization algorithm. Extensive simulations demonstrate that the method is well-suited for the approximation of failure rates. We discuss the extension of such method to a chi-squared independence test adapted to real-time systems.