Hard Real-Time Stationary GANG-Scheduling

The scheduling of parallel real-time tasks enables the efficient utilization of modern multiprocessor platforms for systems with real-time constrains. In this situation, the gang task model, in which each parallel sub-job has to be executed simultaneously, has shown significant performance benefits due to reduced context switches and more efficient intra-task synchronization. In this paper, we provide the first schedulability analysis for sporadic constrained-deadline gang task systems and propose a novel stationary gang scheduling algorithm. We show that the schedulability problem of gang task sets can be reduced to the uniprocessor self-suspension schedulability problem. Furthermore, we provide a class of partitioning algorithms to find a stationary gang assignment and show that it bounds the worst-case interference of each task. To demonstrate the effectiveness of our proposed approach, we evaluate it for implicit-deadline systems using randomized task sets under different settings, showing that our approach outperforms the state-of-the-art

Evaluations of Push Forward: Global Fixed-Priority Scheduling of Arbitrary-Deadline Sporadic Task Systems (Artifact)

This artifact provides the experimental details and implementations of all the facilitated schedulability tests used in the reported acceptance ratio based evaluations as documented in the related paper "Push Forward: Global Fixed-Priority Scheduling of Arbitrary-Deadline Sporadic Task Systems"

Push Forward: Global Fixed-Priority Scheduling of Arbitrary-Deadline Sporadic Task Systems

The sporadic task model is often used to analyze recurrent execution of tasks in real-time systems. A sporadic task defines an infinite sequence of task instances, also called jobs, that arrive under the minimum inter-arrival time constraint. To ensure the system safety, timeliness has to be guaranteed in addition to functional correctness, i.e., all jobs of all tasks have to be finished before the job deadlines. We focus on analyzing arbitrary-deadline task sets on a homogeneous (identical) multiprocessor system under any given global fixed-priority scheduling approach and provide a series of schedulability tests with different tradeoffs between their time complexity and their accuracy. Under the arbitrary-deadline setting, the relative deadline of a task can be longer than the minimum inter-arrival time of the jobs of the task. We show that global deadline-monotonic (DM) scheduling has a speedup bound of 3-1/M against any optimal scheduling algorithms, where M is the number of identical processors, and prove that this bound is asymptotically tight

Reservation-Based Federated Scheduling for Parallel Real-Time Tasks

This paper considers the scheduling of parallel real-time tasks with arbitrary-deadlines. Each job of a parallel task is described as a directed acyclic graph (DAG). In contrast to prior work in this area, where decomposition-based scheduling algorithms are proposed based on the DAG-structure and inter-task interference is analyzed as self-suspending behavior, this paper generalizes the federated scheduling approach. We propose a reservation-based algorithm, called reservation-based federated scheduling, that dominates federated scheduling. We provide general constraints for the design of such systems and prove that reservation-based federated scheduling has a constant speedup factor with respect to any optimal DAG task scheduler. Furthermore, the presented algorithm can be used in conjunction with any scheduler and scheduling analysis suitable for ordinary arbitrary-deadline sporadic task sets, i.e., without parallelism

Timing Analysis of Cause-Effect Chains with Heterogeneous Communication Mechanisms

Software applications in automotive systems are comprised of communicating real-time tasks, described by cause-effect chains. To guarantee functional correctness, it is mandatory to verify end-to-end timing latencies of the cause-effect chains. The analysis of end-to-end latencies highly depends on the communication method. Implicit communication is standardized in the AUTOSAR Timing Specification and ensures data consistency. To abstract communication from the actual execution behavior of tasks, logical execution time (LET) has been proposed. However, the determinism that is provided by LET comes at the cost of increased end-to-end latencies. In industry-grade systems, periodic and sporadic tasks using LET and implicit communication co-exist. Hence, end-to-end latency analyses should cover such heterogeneous cause-effect chains. In this work, we present the first formal analysis framework for end-to-end analysis of cause-effect chains that allows heterogeneous types of recurrent tasks and different communication mechanisms, i.e., (i) a mixed setup of sporadic and periodic tasks that (ii) communicate by a mixed setup of LET and implicit communication mechanism. In this regard, we uncover the principles that homogeneous analyses are built from and discuss how these principles can be transferred to the heterogeneous case. In particular, we cut the cause-effect chain into homogeneous parts which results in 3 different analyses: one baseline approach, one that directly uses the homogeneous results, and one that reduces the pessimism for changes of communication means. Our evaluation shows that for some systems the two more sophisticated approaches outperform the baseline significantly, while for other systems the baseline is already satisfactory

Average Task Execution Time Minimization under (m, k) Soft Error Constraint

Safety-critical systems are often subjected to transient faults. Since these transient faults may lead to soft errors that cause catastrophic consequences, error-handling must be addressed by design. Full-protection against faults is too costly in terms of resource usage. A common approach to relax the resource demands and limit the impact of errors is to consider (m, k)-constraints, which requires that at least m jobs out of any k consecutive jobs are error-free. To assure (m, k)-compliance, static patterns are widely used to select the job execution modes, i.e., either in an error-free mode at the cost of increased worst-case execution time or in an error-prone mode with the advantage of less execution time. Although static patterns have been shown to be effective in energy-aware designs, resource over-provision is inevitable due to the relatively low rate of error probability. In this work, we propose two dynamic (and adaptive) approaches that allow the scheduler to opportunistically select execution modes based on the error-history of the past jobs and the actual error probability. We firstly propose a Markov chain based solution if the error-probability is known and static and secondly a reinforcement learning-based approach that can handle unknown error probabilities. Experimental evaluations show that our approaches outperform the state-of-the-art in most of the evaluated cases in terms of average utilization for each task and the overall utilization for multitask systems