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The shift from single-core to multi-core processors in real-time embedded systems leads to communication based effects on timing such as inter-core communication delays and blocking times. Moreover, the complexity of the scheduling problem increases when multi-core processors are used. In priority-based-scheduling, a fixed priority assignment is used in order to enable predictable behavior of the system. Predictability means that the system has to be analyzable which allows the detection of problems coming from scheduling decisions. For fixed priority scheduling in multi-core real-time embedded systems, a proper task priority assignment has to be done in a way that the system has minimal effects on timing. In this paper, we present an approach for finding near-optimal solutions for task priority assignment and the preemption/cooperation problem. A genetic algorithm is hereby used to create priority assignment solutions. A timing simulator is used for evaluation of each solution regarding real-time properties, memory consumption and communication overhead. In a case study we demonstrate that the proposed approach performs better than well known and single-core optimal heuristics for relatively complex systems.
We present in this paper a new lock-based resource sharing protocol PWLP (Preemptable Waiting Locking Protocol) for embedded multi-core processors. It is based on the busy-wait model and works with non-preemptive critical sections while task may be preempted by tasks with a higher priority when waiting for resources. Our protocol can be applied in partitioned as well as global scheduling scenarios, in which task-fix, job-fix or dynamically assigned priorities may be used. Furthermore, the PWLP permits nested requests to shared resources. Finally, we present a case study based on event-based simulations in which the FMLP (Flexible Multiprocessor Locking Protocol) and the proposed PWLP are compared.
Extended Task Priority and Preemptability Optimization in Real-Time Multi-Core Embedded Systems
(2014)
We present a model-based optimization approach for the task allocation problem in embedded multi-core systems. The information therefore is obtained from a system description in AUTOSAR and runtime measurements of the runnables in hardware traces. Based on this an initial software partitioning of runnables to tasks is created. We then use a genetic algorithm to create and evaluate solutions to the task allocation problem. Each solution is hereby evaluated using a discrete event-based simulation, which allows the evaluation with regard to real-time properties, resource consumption and data-communication overhead. The significance of our approach is then shown in a case-study. There, we optimize the task allocation of an embedded system, whose complexity is comparable to that of an actual system, on a multi-core processor. Finally, the results of the optimization are transferred to an ECU Configuration Description to enable further development in compliance with the AUTOSAR methodology.
Traditional methods rely on Static Timing Analysis techniques to compute the Worst Case Response Time for tasks in real-time systems. Multi-Core real-time systems are faced up with concurrent task executions, semaphore accesses, and task migrations where it may be difficult to obtain the worst case upper bound. A new three staged probabilistic estimation concept is presented. Worst Case Response Times are estimated for tasksets which consist of tasks with multiple time bases. The concept involves data generation with sample classification and sample size equalization, model fit and Worst Case Response Time estimation on the basis of extreme value distribution models. A Generalized Pareto Distribution model fit method which includes threshold detection and parameter estimation is also presented. Sample classification in combination with the new Generalized Pareto Distribution model fit method allows to estimate Worst Case Response Times with low pessimism ranges compared to estimation methods that uses the Generalized Pareto or the Gumbel max distribution without sample classification.
With the availability of the AUTOSAR standard, model-driven methodologies are becoming established in theautomotive domain. However, the process of creating models ofexisting system components is often difficult and time consuming, especially when legacy code has to be re-used or informationabout the exact timing behavior is needed. In order to tackle thisreverse engineering problem, we present CoreTAna, a novel toolthat derives an AUTOSAR compliant model of a real-time systemfrom a dynamic analysis of its trace recordings. This paper givesan overview of CoreTAna's current features and discusses itsbenefits for reverse engineering.
Der Embedded Markt stellt sich auf eine neue Herausforderung ein: denUmstieg von Singlecore- auf Multicore-Prozessorsysteme. Dabei soll dieUmsetzung der Norm ISO 26262 die Funktionale Sicherheit der elektri-schen und elektronischen Systeme im Kraftfahrzeug gewährleisten. In diesem Beitrag betrachten die Hochschule Regensburg und die TÜV SüdAutomotive GmbH das Scheduling eines Echtzeitsystems als ein sicher-heitsrelevantes Sub-System.