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We address a novel probabilistic approach to estimate the Worst Case Response Time boundaries of tasks. Multi-core real-time systems process tasks in parallel on two or more cores. Tasks in our contribution may preempt other tasks, block tasks with semaphores to access global shared resources, or migrate to another core. The depicted task behavior is random. The shape of collected response times of a task within a processing time is multimodal. Extreme Value approaches need unimodal response time distributions to estimate the Worst Case Response Time of tasks. The new proposed method derives a set of three task set shapes from the source task set. It is used to minimize the uncertainty of random task behavior by maximizing the coverage of possible Worst Case Response Times. The case study evaluates the new proposed estimation method by the use of dynamically generated random tasks with varying task properties.
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.
Global scheduling algorithms are very promising for application in embedded real-time systems using multi-core controllers. In this paper we want to make a first step forward to apply such scheduling methods on real existing systems. Especially a new resource model is necessary to avoid deadlocks, as this goal can not be achieved by using the standard OSEK Priority Ceiling Protocol when shared global resources are in use. We also introduce the new metric mean Normalized Blocking Time in order to be able to compare locking mechanisms according to the timing effects of their blocking behavior. Finally we give a simulative application example of the new metric by the use of two different kinds of semaphore models and an example task set typical for existing embedded real-time systems in the automotive powertrain environment.
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.
Safe and reliable multi-core technology becomes more and more important in the field of embedded systems. Today’s and future embedded systems require increasing performance while being more energy efficient. Moreover, the functional safety for these embedded systems has to be improved or developed completely new. In this chapter, we first address the challenges of embedded multi-core real-time systems. To raise the resilience of such systems we use the deadlock-free synchronization model of Block et al. (A flexible real-time locking protocol for multiprocessors. 2012 IEEE international conference on embedded and real-time computing systems and applications, vol 0, pp 47–56, 2007). The metric mean Normalized Blocking Time (mNBT) is hereby used to measure the timing effects of the blocking behavior of strongly interacting tasks. In a second step, we present a model-based approach to map the tasks of an embedded real-time system to the cores of a multi-core processor. Moreover, we derive an execution time model from runtime measurements of software functions. This information is then used to perform precise probabilistic simulations of different task-to-core mappings and evaluate them with regard to task response times, inter-task blocking overhead and load distribution. Subsequently, we integrate the probabilistic simulation within an optimization technique to systematically improve the task-to-core mapping. We conclude with a case-study, where we demonstrate the effectiveness of the presented approach by optimizing the task-to-core mapping of a practical automotive powertrain system.