Prestandaoptimerande prioritetstilldelning i inbyggda applikationer med mjuka realtidskrav

Optimizing task priority assignments is a well-researched area in the context of hard real-time systems, where the goal in the majority of cases is to produce a priority assignment that results in a schedulable task set. The problem has also been considered, albeit not to the same extent, in the soft real-time context where quality of service metrics determine the overall performance of systems. Previous research on the problem in the soft real-time context often resorts to some analytical approach, with the drawback of having to put relatively strict constraints on the system models to avoid excessively complex analysis computations. As a consequence, many attributes of a real system have to be omitted, and features such as multi-processor hardware platforms might make the analytical approach unfeasible due to complexity issues. In this thesis we took a different approach to the problem and used discrete event simulation to drive the priority assignment optimization process, which enabled more complex system models at the cost of increased objective function evaluation times. A latency-related quality of service metric was used as the objective function in a tabu search based optimization heuristic. Improvements were observed in both simulation and in the real system that was modeled. The results show that the model successfully captured key attributes of the modeled system, and that the discrete event simulation approach is a viable option when the goal is to improve or determine the quality of service of a soft real-time application