Preemption-Delay Aware Schedulability Analysis of Real-Time Systems

Schedulability analysis of real-time systems under preemptive scheduling may often lead to false-negative results, deeming a schedulable taskset being unschedulable. This is the case due to the inherent over-approximation of many time-related parameters such as task execution time, system delays, etc., but also, in the context of preemptive scheduling, a significant over-approximation arises from accounting for task preemptions and corresponding preemption-related delays. To reduce false-negative schedulability results, it is highly important to as accurately as possible approximate preemption-related delays. Also, it is important to obtain safe approximations, which means that compared to the approximated delay, no higher corresponding delay can occur at runtime since such case may lead to false-positive schedulability results that can critically impact the analysed system. Therefore, the overall goal of this thesis is: To improve the accuracy of schedulability analyses to identify schedulable tasksets in real-time systems under fixed-priority preemptive scheduling, by accounting for tight and safe approximations of preemption-related delays. We contribute to the domain of timing analysis for single-core real-time systems under preemptive scheduling by proposing two novel cache-aware schedulability analyses: one for fully-preemptive tasks, and one for tasks with fixed preemption points. Also, we propose a novel method for deriving safe and tight upper bounds on cache-related preemption delay of tasks with fixed preemption points. Finally, we contribute to the domain of multi-core partitioned hard real-time systems by proposing a novel partitioning criterion for worst-fit decreasing partitioning, and by investigating the effectiveness of different partitioning strategies to provide task allocation which does not jeopardize the schedulability of a taskset in the context of preemptive~scheduling