Design Optimization of Soft Real-Time Applications on FlexRay Platforms

FlexRay is a deterministic communication bus in the automotive context that supports fault-tolerant and high-speed bus system. It operates based on the time-division-multiple-access scheme and allows transmission of event-driven and time-driven messages between nodes in a system. A FlexRay bus has two periodic segments which form a bus cycle: static segment and dynamic segment. Such a bus system could be used in a wide area of real-time automotive applications with soft and hard timing constraints. Recent research has been focused on the FlexRay static segment. As opposed to the static segment, however, the dynamic one is based on an event-triggered scheme. This scheme is more difficult to be temporally predicted. Nevertheless, the event-triggered paradigm provides more flexibility for further incremental design. The dynamic segment is also suitable for applications with erratic data size. Such advantages motivate for more research on the dynamic segment. In a real-time system, results of the computations have to be ready by a specific instant of time called deadline . However, in a soft real-time application, the result can be used with a degraded Quality of Service even after the deadline has passed while in a hard real-time system, missing a deadline leads to a catastrophe. This thesis aims at optimizing some of the parameters of the FlexRay bus for soft real-time applications. The cost function which helps to assess the solution to the optimization problem is the deadline miss ratio and a solution to our problem consists of two parts: (1) Frame identifiers to messages which are produced at each node. (2) The size of each individual minislot which is one of the FlexRay bus parameters. The optimization is done based on genetic algorithms. To evaluate the proposed approach, several experiments have been conducted based on the FlexRay bus simulator implemented in this thesis. The achieved results show that suitable choice of the parameters which are generated by our optimization engine improves the timing behavior of simulated communicating nodes