Statistical-Based Response-Time Analysis of Systems with Execution Dependencies between Tasks
This paper presents a novel statistical-based approach to Worst-Case Response-Time (WCRT) analysis of complex real-time system models. These system models have been tailored to capture intricate execution dependencies between tasks, inspired by real industrial control systems. The proposed WCRT estimation algorithm is based on Extreme Value Theory (EVT) and produces both WCRT estimates together with a probability of being exceeded. By using the tools developed, an evaluation is presented using three different simulation models, and four other methods as reference: Monte Carlo simulation, MABERA, HCRR and traditional Response-Time Analysis (basic RTA). Empirical results demonstrate that the benefit of the proposed approach, in terms of 1) reduced pessimism when compared to basic RTA and 2) validated guarantee of never being less than the actual response time values. The proposed approach also needs much fewer simulations compared to other three simulation-based methods.