Motivated by the lack of response-time analyses for non-preemptive global scheduling that consider shared resources, this paper provides such an analysis for global job-level fixed-priority (JLFP) scheduling policies and FIFO-ordered spin locks. The proposed analysis computes response-time bounds for a set of resource-sharing jobs subject to release jitter and execution-time uncertainties by implicitly exploring all possible execution scenarios using state-abstraction and state-pruning techniques. A large-scale empirical evaluation of the proposed analysis shows it to be substantially less pessimistic than simple execution-time inflation methods, thanks to the explicit modeling of contention for shared resources and scenario-aware blocking analysis.

Complex real-time systems are traditionally developed in several disjoint steps: (i) decomposition of applications into sets of recurrent tasks, (ii) worst-case execution time estimation, and (iii) schedulability analysis. Each step is already in itself complex and error-prone, and the composition of all three poses a nontrivial integration problem. In particular, it is challenging to obtain an end-to-end analysis of timing properties of the whole system due to practical differences between the interfaces of tools for extracting task models, execution time analysis, and schedulability tests. To address this problem, we propose a seamless and pragmatic end-to-end compilation and timing analysis toolchain, where source programs are written in a real-time extension of C, called Timed C. The toolchain automatically translates timing primitives into executable code, measures execution times, and verifies temporal correctness using an extended schedulability test for non-preemptive generalized multiframe task sets. Novel aspects of our approach are: (i) both soft and firm tasks can be expressed at the programming language level and stated timing requirements are automatically verified by the schedulability test, and (ii) the schedulability test outputs per-job response-time information that enables a new approach to sensitivity analysis. Specifically, we perform a weakly hard sensitivity analysis that determines the worst-case execution time margins for the strongest still-satisfied (M,K) constraint, where M = m1 +...+ mN denotes the number of deadline misses across the entire task set, and K = {k1,..., kN} is the set of windows of interest of the different tasks. The toolchain is implemented as a source-to-source compiler, freely available as open source, and conveniently distributed as a Docker container.

Most recurrent real-time applications can be modeled as a set of sequential code segments (or blocks) that must be (repeatedly) executed in a specific order. This paper provides a schedulability analysis for such systems modeled as a set of parallel DAG tasks executed under any limited-preemptive global job-level fixed priority scheduling policy. More precisely, we derive response-time bounds for a set of jobs subject to precedence constraints, release jitter, and execution-time uncertainty, which enables support for a wide variety of parallel, limited-preemptive execution models (e.g., periodic DAG tasks, transactional tasks, generalized multi-frame tasks, etc.). Our analysis explores the space of all possible schedules using a powerful new state abstraction and state-pruning technique. An empirical evaluation shows the analysis to identify between 10 to 90 percentage points more schedulable task sets than the state-of-the-art schedulability test for limited-preemptive sporadic DAG tasks. It scales to systems of up to 64 cores with 20 DAG tasks. Moreover, while our analysis is almost as accurate as the state-of-the-art exact schedulability test based on model checking (for sequential non-preemptive tasks), it is three orders of magnitude faster and hence capable of analyzing task sets with more than 60 tasks on 8 cores in a few seconds.